1. A method for developing a predictive maintenance system for industrial equipment using machine learning algorithms, comprising: collecting data from sensors placed on the industrial equipment to monitor its performance and condition; preprocessing the collected data by removing noise, outliers, and missing values; feature engineering to extract relevant features from the preprocessed data; applying a machine learning algorithm to the extracted features to train a predictive model that can forecast future equipment failures; validating the accuracy and performance of the trained predictive model using test data; and deploying the predictive maintenance system on the industrial equipment to monitor its condition and predict equipment failures before they occur.

2. The method of claim 1, wherein the machine learning algorithm used to train the predictive model is a deep learning algorithm that employs neural networks to learn complex patterns and relationships in the collected data.

3. The method of claim 1, wherein the predictive maintenance system is integrated with a cloud-based platform that can remotely monitor the performance of the industrial equipment and provide real-time alerts and notifications to maintenance personnel.

4. A system for developing a predictive maintenance system for industrial equipment using machine learning algorithms, comprising: a data collection module configured to collect data from sensors placed on the industrial equipment to monitor its performance and condition; a data preprocessing module configured to preprocess the collected data by removing noise, outliers, and missing values; a feature engineering module configured to extract relevant features from the preprocessed data; a machine learning module configured to apply a machine learning algorithm to the extracted features to train a predictive model that can forecast future equipment failures; a validation module configured to validate the accuracy and performance of the trained predictive model using test data; and a deployment module configured to deploy the predictive maintenance system on the industrial equipment to monitor its condition and predict equipment failures before they occur.

5. The system of claim 4, wherein the data collection module comprises one or more sensors selected from the group consisting of temperature sensors, pressure sensors, vibration sensors, acoustic sensors, and current sensors.

6. The system of claim 4, wherein the data preprocessing module comprises one or more techniques selected from the group consisting of data cleaning, data normalization, data transformation, and data imputation.

7. The system of claim 4, wherein the feature engineering module comprises one or more techniques selected from the group consisting of statistical analysis, time-series analysis, Fourier analysis, wavelet analysis, and principal component analysis.

8. The system of claim 4, wherein the machine learning module comprises one or more algorithms selected from the group consisting of decision trees, random forests, support vector machines, artificial neural networks, and deep learning models.

9. The system of claim 4, wherein the validation module comprises one or more metrics selected from the group consisting of accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve, and mean squared error.

10. The system of claim 4, wherein the deployment module comprises one or more methods selected from the group consisting of cloud-based deployment, edge-based deployment, and hybrid deployment. DEVELOPMENT OF A PREDICTIVE MAINTENANCE SYSTEM FOR INDUSTRIAL EQUIPMENT USING MACHINE LEARNING ALGORITHMS Abstract A method for developing a predictive maintenance system for industrial equipment using machine learning algorithms may be included in embodiments of the present disclosure. This method may include collecting data from sensors placed on the industrial equipment to monitor its performance and condition. In certain embodiments, there is additionally a step called preprocessing, which involves eliminating noise, outliers, and missing values from the data that was gathered. In certain embodiments, there is also the possibility of feature engineering, which is the process of extracting useful features from preprocessed data. In certain embodiments, there is also the possibility of using a machine learning algorithm on the extracted information in order to build a predictive model that may anticipate upcoming failures in equipment. Validating the correctness and performance of the trained prediction model by utilizing test data is another component that may be included in embodiments. In certain embodiments, it may also be possible to implement a predictive maintenance system on industrial equipment in order to monitor the status of such equipment and anticipate breakdowns in advance of their occurrence. , Claims:Claims :

1. A method for developing a predictive maintenance system for industrial equipment using machine learning algorithms, comprising: collecting data from sensors placed on the industrial equipment to monitor its performance and condition; preprocessing the collected data by removing noise, outliers, and missing values; feature engineering to extract relevant features from the preprocessed data; applying a machine learning algorithm to the extracted features to train a predictive model that can forecast future equipment failures; validating the accuracy and performance of the trained predictive model using test data; and deploying the predictive maintenance system on the industrial equipment to monitor its condition and predict equipment failures before they occur.

2. The method of claim 1, wherein the machine learning algorithm used to train the predictive model is a deep learning algorithm that employs neural networks to learn complex patterns and relationships in the collected data.

3. The method of claim 1, wherein the predictive maintenance system is integrated with a cloud-based platform that can remotely monitor the performance of the industrial equipment and provide real-time alerts and notifications to maintenance personnel.

4. A system for developing a predictive maintenance system for industrial equipment using machine learning algorithms, comprising: a data collection module configured to collect data from sensors placed on the industrial equipment to monitor its performance and condition; a data preprocessing module configured to preprocess the collected data by removing noise, outliers, and missing values; a feature engineering module configured to extract relevant features from the preprocessed data; a machine learning module configured to apply a machine learning algorithm to the extracted features to train a predictive model that can forecast future equipment failures; a validation module configured to validate the accuracy and performance of the trained predictive model using test data; and a deployment module configured to deploy the predictive maintenance system on the industrial equipment to monitor its condition and predict equipment failures before they occur.

5. The system of claim 4, wherein the data collection module comprises one or more sensors selected from the group consisting of temperature sensors, pressure sensors, vibration sensors, acoustic sensors, and current sensors.

6. The system of claim 4, wherein the data preprocessing module comprises one or more techniques selected from the group consisting of data cleaning, data normalization, data transformation, and data imputation.

7. The system of claim 4, wherein the feature engineering module comprises one or more techniques selected from the group consisting of statistical analysis, time-series analysis, Fourier analysis, wavelet analysis, and principal component analysis.

8. The system of claim 4, wherein the machine learning module comprises one or more algorithms selected from the group consisting of decision trees, random forests, support vector machines, artificial neural networks, and deep learning models.

9. The system of claim 4, wherein the validation module comprises one or more metrics selected from the group consisting of accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve, and mean squared error.

10. The system of claim 4, wherein the deployment module comprises one or more methods selected from the group consisting of cloud-based deployment, edge-based deployment, and hybrid deployment.

Description:DEVELOPMENT OF A PREDICTIVE MAINTENANCE SYSTEM FOR INDUSTRIAL EQUIPMENT USING MACHINE LEARNING ALGORITHMS
Field of the Invention
[0001] The present invention relates to a predictive maintenance system for industrial equipment, specifically using machine learning algorithms to analyze data from various sources and accurately predict potential equipment failures. The system provides actionable insights for maintenance interventions, allowing for targeted and efficient maintenance that minimizes unplanned downtime and maximizes equipment efficiency. The invention is applicable to a wide range of industrial equipment and industries and is designed to be easily scalable and adaptable to different types of equipment and operating environments.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] In many industrial settings, the efficient and reliable operation of equipment is critical to maintaining production and minimizing downtime. However, equipment failures and breakdowns can occur unexpectedly, leading to unplanned maintenance and downtime. Traditional maintenance strategies involve performing routine maintenance on a set schedule, which may not be optimized for the specific needs of each individual piece of equipment. Predictive maintenance is an approach that aims to address this issue by using data analysis and machine learning to identify potential equipment failures before they occur, allowing for more targeted maintenance interventions and reducing unplanned downtime.
[0004] Prior art in the field of predictive maintenance includes various types of sensors, data analytics software, and machine learning algorithms that can be used to analyze data from equipment in real-time. Few of the prior arts are listed below.
[0005] CN110719210B (By: GANJIANG NEW AREA WISDOM IOT RESEARCH INSTITUTE) The invention relates to the technical field of cloud computing and edge computing, and aims to provide an industrial equipment predictive maintenance method based on cloud-edge cooperation. The invention discloses an industrial equipment predictive maintenance method based on cloud-side cooperation, and the method comprises the following steps: S1, heterogeneous sensing equipment collects the equipment state data of industrial equipment, and transmitsg the equipment state data to an edge computing platform; s2, an edge data management module of an edge computing platform obtains feature datarequired by a prediction task of the target equipment according to data uploaded by the heterogeneous sensing equipment; a configuration reloading module of a prediction service orchestrator obtains equipment state prediction model configuration of the cloud computing platform and equipment state prediction model configuration trained by an edge model training module, a model operation module loads a latest equipment state prediction model of target equipment, and extracted feature data is input; whether the target equipment has a fault risk or not is judged according to the output data of themodel operation module, if so, the next step is executed, and if not, the step S1 is returned; and S3, a trigger management module of the edge computing platform notifies a designated person in charge of fault early warning information according to a preset trigger. According to the invention, accurate and efficient industrial equipment predictive maintenance can be realized.
[0006] JP2022078082A (By: STRONG FORCE IOT PORTFOLIO 2016) PROBLEM TO BE SOLVED: To provide a method and system for collecting data in an industrial environment, and a method and system for using the collected data. SOLUTION: A system includes: a platform including a computing environment connected to a local data collection system having a first sensor signal and a second sensor signal from a first machine; a first sensor within the local data collection system connected to the first machine; a second sensor within the local data collection system; and a crosspoint switch having a plurality of inputs and a plurality of outputs including a first input of the first sensor and a second input of the second sensor. The plurality of outputs are configured to be switchable between a condition in which the first output is switched between delivery of the first sensor signal and delivery of the second sensor signal, and a condition in which there is simultaneous delivery of the first sensor signal from the first output and the second sensor signal from the second output.
[0007] US20200067789A1 (By: QIO TECHNOLOGY) The foregoing are among the objects attained by the invention which provides cloud native distributed, hierarchical methods and apparatus for the ingestion of data generated by a fully-instrumented manufacturing or industrial plants. The systems and methods employ an architecture that is capable of collecting and preliminarily processing data at the plant-level for self-learning detection of error (and other) conditions, and forwarding that data for more in depth processing in the cloud. The architecture takes into account the varied data throughput, storage and processing needs at each level of the hierarchy. The distributed and hierarchical system allows for the creation of a dynamic, real-time assessment of the behavior and health of assets and enables visibility and integrity into the design, manufacturing, operations and service of any asset. The use of that capability (referred to herein as PARCS™) allows for Systemic Asset Intelligence within an asset, plant, system and/or an ecosystem.
[0008] However, existing systems may be limited in their ability to accurately predict equipment failures or may require significant manual intervention to analyze and interpret the data. Additionally, existing systems may not be easily scalable or adaptable to different types of equipment or industries.
[0009] Therefore, there is a need for a predictive maintenance system that can accurately predict equipment failures, require minimal manual intervention, and be easily scalable and adaptable to different types of equipment and industries. The present invention aims to address these needs by providing a novel predictive maintenance system that utilizes a combination of machine learning algorithms and data from various sources to accurately predict equipment failures and provide actionable insights for maintenance interventions.
[00010] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
[00011] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00012] The present invention relates to a predictive maintenance system for industrial equipment, specifically using machine learning algorithms to analyze data from various sources and accurately predict potential equipment failures. The system provides actionable insights for maintenance interventions, allowing for targeted and efficient maintenance that minimizes unplanned downtime and maximizes equipment efficiency. The invention is applicable to a wide range of industrial equipment and industries and is designed to be easily scalable and adaptable to different types of equipment and operating environments.
[00013] Embodiments of the present disclosure may include a method for developing a predictive maintenance system for industrial equipment using machine learning algorithms, wherein the method includes collecting data from sensors placed on the industrial equipment to monitor its performance and condition. Embodiments may also include preprocessing the collected data by removing noise, outliers, and missing values.
[00014] Embodiments may also include feature engineering to extract relevant features from the preprocessed data. Embodiments may also include applying a machine learning algorithm to the extracted features to train a predictive model that can forecast future equipment failures. Embodiments may also include validating the accuracy and performance of the trained predictive model using test data. Embodiments may also include deploying the predictive maintenance system on the industrial equipment to monitor its condition and predict equipment failures before they occur.
[00015] In some embodiments, the machine learning algorithm used to train the predictive model may be a deep learning algorithm that employs neural networks to learn complex patterns and relationships in the collected data. In some embodiments, the predictive maintenance system may be integrated with a cloud-based platform that can remotely monitor the performance of the industrial equipment and provide real-time alerts and notifications to maintenance personnel.
[00016] Embodiments of the present disclosure may also include a system for developing a predictive maintenance system for industrial equipment using machine learning algorithms, including a data collection module configured to collect data from sensors placed on the industrial equipment to monitor its performance and condition. Embodiments may also include a data preprocessing module configured to preprocess the collected data by removing noise, outliers, and missing values.
[00017] Embodiments may also include a feature engineering module configured to extract relevant features from the preprocessed data. Embodiments may also include a machine learning module configured to apply a machine learning algorithm to the extracted features to train a predictive model that can forecast future equipment failures. Embodiments may also include a validation module configured to validate the accuracy and performance of the trained predictive model using test data. Embodiments may also include a deployment module configured to deploy the predictive maintenance system on the industrial equipment to monitor its condition and predict equipment failures before they occur.
[00018] In some embodiments, the data collection module may include one or more sensors selected from the group consisting of temperature sensors, pressure sensors, vibration sensors, acoustic sensors, and current sensors. In some embodiments, the data preprocessing module may include one or more techniques selected from the group consisting of data cleaning, data normalization, data transformation, and data imputation.
[00019] In some embodiments, the feature engineering module may include one or more techniques selected from the group consisting of statistical analysis, time-series analysis, Fourier analysis, wavelet analysis, and principal component analysis. In some embodiments, the machine learning module may include one or more algorithms selected from the group consisting of decision trees, random forests, support vector machines, artificial neural networks, and deep learning models.
[00020] In some embodiments, the validation module may include one or more metrics selected from the group consisting of accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve, and mean squared error. In some embodiments, the deployment module may include one or more methods selected from the group consisting of cloud-based deployment, edge-based deployment, and hybrid deployment.
Brief Description of the Drawings
[00021] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00022] FIG. 1 is a flowchart illustrating a method for developing a predictive maintenance system for industrial equipment, according to some embodiments of the present disclosure.
[00023] FIG. 2 is a block diagram illustrating a system for developing a predictive maintenance system for industrial equipment, according to some embodiments of the present disclosure.
[00024] FIG. 3 is a block diagram further illustrating the system from FIG. 2 for developing a predictive maintenance system for industrial equipment, according to some embodiments of the present disclosure.
[00025] FIG. 4 is a block diagram further illustrating the system from FIG. 2 for developing a predictive maintenance system for industrial equipment, according to some embodiments of the present disclosure.
[00026] FIG. 5 is a block diagram further illustrating the system from FIG. 2 for developing a predictive maintenance system for industrial equipment, according to some embodiments of the present disclosure.
[00027] FIG. 6 is a block diagram further illustrating the system from FIG. 2 for developing a predictive maintenance system for industrial equipment, according to some embodiments of the present disclosure.
[00028] FIG. 7 is a block diagram further illustrating the system from FIG. 2 for developing a predictive maintenance system for industrial equipment, according to some embodiments of the present disclosure.
[00029] FIG. 8 is a block diagram further illustrating the system from FIG. 2 for developing a predictive maintenance system for industrial equipment, according to some embodiments of the present disclosure.
[00030]
Detailed Description
[00031] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00032] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00033] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items..
[00034] The present invention relates to a predictive maintenance system for industrial equipment, specifically using machine learning algorithms to analyze data from various sources and accurately predict potential equipment failures. The system provides actionable insights for maintenance interventions, allowing for targeted and efficient maintenance that minimizes unplanned downtime and maximizes equipment efficiency. The invention is applicable to a wide range of industrial equipment and industries and is designed to be easily scalable and adaptable to different types of equipment and operating environments.
[00035] In Figure 1, a flowchart depicts the process of developing a predictive maintenance system for industrial equipment. This figure also offers a description of the method in line with different features of the current disclosure. Step 110 may, in certain implementations of the method, entail the process of collecting data from sensors that have been connected to the industrial equipment in order to monitor its operation and status. This might be the case in some of the ways in which the method is implemented. In the approach, step 120 may entail cleaning the obtained data by removing noise, outliers, and values that are missing. The method could include feature engineering as step 130, which refers to the process of extracting and using significant features from preprocessed data. The method may include, at step 140, the application of a machine learning algorithm to the extracted information in order to develop a predictive model that may forecast future failures in equipment. In this way, the method can prevent unexpected breakdowns from occurring. Using test data, the procedure may, at step 150, comprise testing the efficacy and accuracy of the trained prediction model. This verification can take place using test data. The deployment of the predictive maintenance system on the industrial equipment may be step 160 of the technique. This is done so that the state of the equipment can be monitored and defects in the equipment may be anticipated before they occur.
[00036] In certain implementations, the machine learning algorithm that is used to train the predictive model may be a deep learning algorithm. This is because deep learning is considered to be more advanced than traditional machine learning. This particular kind of algorithm makes use of neural networks in order to identify more complex patterns and correlations in the data that has been obtained. According to some implementations of the predictive maintenance system, a cloud-based platform that can remotely monitor the performance of industrial equipment and provide real-time alerts and notifications to maintenance personnel may be integrated with the system so that it can function in the manner that was described above.
[00037] As a block diagram, the system 200 (for developing a predictive maintenance system for industrial equipment) is shown in FIG. 2, which also offers a description of the system in line with different features of the present disclosure. The following are some examples of modules that the system 200 could have, depending on the implementation: a machine learning module 240 that is programmed to apply a machine learning algorithm to the extracted features in order to train a predictive model that is able to forecast future failures of the industrial equipment; a data collection module 210 that is programmed to collect data from sensors that are attached to the industrial equipment in order to monitor its operation and condition; a feature engineering module 230 that is programmed to extract pertinent features from the preprocessed data; and a data preprocessing module 220 may also be included in the system 200. This module's purpose is to remove noise, outliers, and values that do not exist from the data that has been obtained.
[00038] A more in-depth explanation of the system 200 seen in FIG. 2 may be found in FIG. 3, which is a block diagram that depicts numerous different ways in which the present disclosure can be put into practice. It's conceivable that the data collecting module 210 in certain implementations will contain one or more of the sensors 311 that were selected from the group. Some of the kind of sensors that might be included in the one or more sensors 311 are as follows: temperature sensors 312, pressure sensors 313, vibration sensors 314, sound sensors 315, and current sensors 316.
[00039] The system 200 seen in Figure 2 is broken down into its component parts in more detail in the block diagram that may be found in FIG. 4. This is done in line with specific applications of the current disclosure for developing a predictive maintenance system for industrial equipment. In some implementations, the data preparation module could have a role to play. It is conceivable that data transformation 424 and data imputation 426 are components of one or more of the techniques 420 that have been described. In addition, the process of data cleaning and normalisation 422 could also be included as one of the techniques 420.
[00040] A more in-depth explanation of the system 200 that is shown in Figure 2 may be found in Figure 5, which is a block diagram that depicts numerous different ways in which the present disclosure can be put into practise. It's possible that some implementations of the feature engineering module 230 will include one or more of the techniques 551 that were picked from the group. One or more of the techniques 551 at play here might include analyses such as statistical analysis 552, time-series analysis 553, Fourier analysis 554, wavelet analysis 555, and principal component analysis 556.
[00041] A more in-depth explanation of the system 200 that is shown in Figure 2 may be found in Figure 6, which is a block diagram that depicts numerous different ways in which the present disclosure can be put into practise. The machine learning module 240 in certain implementations may make the decision to include one or more of the algorithms 642 from the group into its functionality. It's possible that decision trees 644 are one of the several algorithms 642 that are now being used. Deep learning models might be included in the one or more algorithms 642 in addition to random forests 646, support vector machines, artificial neural networks, and deep learning models.
[00042] In accordance with some applications of the current disclosure, the block diagram shown in Figure 7 provides a more in-depth look at the system 200 shown in Figure 2. One or more of the metrics 761 described earlier might be included in some implementations of the validation module 250. There are a number of measures that might be considered for inclusion in the one or more metrics 761, including accuracy 762, precision 763, recall 764, and F1-score 765. It's possible that the area 766 under the receiver

operating characteristic curve and the mean squared error are two of the metrics 761 that are being debated right now.
[00043] In accordance with some applications of the current disclosure, the block diagram shown in Figure 8 provides a more in-depth look at the system 200 shown in Figure 2. It is feasible that the deployment module 260 of some implementations will have hybrid deployment 864 in its architecture. It is possible for the deployment module 260 to provide cloud-based deployment in addition to cloud- and edge-based deployment 862. One or more strategies chosen from among the available possibilities presented by the group.
[00044] The method and system described in the given claims provide a comprehensive solution for developing a predictive maintenance system for industrial equipment using machine learning algorithms. The system is designed to collect data from various sensors placed on the equipment to monitor its performance and condition, preprocess the collected data to remove any noise, outliers, and missing values, and then extract relevant features from the preprocessed data. Machine learning algorithms are then applied to the extracted features to train a predictive model that can forecast future equipment failures.
[00045] The system includes a data collection module that collects data from various sensors, including temperature sensors, pressure sensors, vibration sensors, acoustic sensors, and current sensors. The collected data is then preprocessed using techniques such as data cleaning, data normalization, data transformation, and data imputation. The feature engineering module is then used to extract relevant features from the preprocessed data using statistical analysis, time-series analysis, Fourier analysis, wavelet analysis, and principal component analysis.
[00046] The machine learning module then applies various algorithms such as decision trees, random forests, support vector machines, artificial neural networks, and deep learning models to the extracted features to train a predictive model. The accuracy and performance of the trained model are then validated using test data, which includes metrics such as accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve, and mean squared error.
[00047] Finally, the deployment module deploys the predictive maintenance system on the industrial equipment to monitor its condition and predict equipment failures before they occur. The system can be integrated with a cloud-based platform that can remotely monitor the equipment's performance and provide real-time alerts and notifications to maintenance personnel. The deployment module can be customized using various methods such as cloud-based deployment, edge-based deployment, and hybrid deployment, depending on the specific requirements of the industrial equipment.
[00048] In summary, the method and system described in the given claims provide an effective solution for developing a predictive maintenance system for industrial equipment using machine learning algorithms. The system is designed to collect data from various sensors, preprocess the data, extract relevant features, train a predictive model, validate the accuracy and performance of the model, and deploy the system to monitor the equipment's condition and predict equipment failures before they occur.
[00049] A method for developing a predictive maintenance system for industrial equipment using machine learning algorithms may be included in embodiments of the present disclosure. This method may include collecting data from sensors placed on the industrial equipment to monitor its performance and condition. In certain embodiments, there is additionally a step called preprocessing, which involves eliminating noise, outliers, and missing values from the data that was gathered.
[00050] In certain embodiments, there is also the possibility of feature engineering, which is the process of extracting useful features from preprocessed data. In certain embodiments, there is also the possibility of using a machine learning algorithm on the extracted information in order to build a predictive model that may anticipate upcoming failures in equipment. Validating the correctness and performance of the trained prediction model by utilising test data is another component that may be included in embodiments. In certain embodiments, it may also be possible to implement a predictive maintenance system on industrial equipment in order to monitor the status of such equipment and anticipate breakdowns in advance of their occurrence.
[00051] In some implementations, the machine learning algorithm that is used to train the predictive model may be a deep learning algorithm. This kind of algorithm makes use of neural networks to discover more complicated patterns and correlations in the data that has been gathered. A cloud-based platform that can remotely monitor the performance of the industrial equipment and provide real-time alerts and notifications to maintenance personnel may, according to some implementations of the predictive maintenance system, be integrated with the system so as to make it possible for it to function in the manner described above.
[00052] A system for developing a predictive maintenance system for industrial equipment using machine learning algorithms may also be included among the embodiments of the present disclosure. This system may include a data collection module that is designed to collect data from sensors that have been placed on the industrial equipment in order to monitor its performance and condition. A data pretreatment module that is set up to preprocess the data that has been gathered by eliminating noise, outliers, and missing values is another thing that embodiments could include.
[00053] A feature engineering module that is set up to extract important features from the preprocessed data may also be included in embodiments. In certain embodiments, there is additionally a machine learning module that is designed to apply a machine learning algorithm to the extracted characteristics in order to train a predictive model that can predict when certain pieces of equipment may fail in the future. A validation module that is set up to verify the correctness and performance of the trained prediction model using test data may also be included in embodiments. Some embodiments may further comprise a deployment module that is set up to install the predictive maintenance system on the industrial equipment in order to monitor the status of the equipment and forecast when it will break down before it does so.
[00054] The data collecting module may, in certain implementations, comprise one or more sensors chosen from the group consisting of temperature sensors, pressure sensors, vibration sensors, audio sensors, and current sensors. Alternatively, the module may not include any sensors at all. The data preparation module may, in certain implementations, comprise one or more methods chosen from the category consisting of data cleaning, data normalisation, data transformation, and data imputation. In other words, the approaches can be used in any combination.
[00055] In some implementations, the feature engineering module could include one or more methods chosen from the category consisting of statistical analysis, time-series analysis, Fourier analysis, wavelet analysis, and principal component analysis. A decision tree, a random forest, a support vector machine, an artificial neural network, and a deep learning model are all examples of the types of algorithms that might be included in the machine learning module of some embodiments.
[00056] In certain implementations, the validation module may include one or more metrics chosen from the group consisting of accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve, and mean squared error. Alternatively, the validation module may not include any metrics at all. In some implementations, the deployment module may include one or more ways chosen from the group consisting of cloud-based deployment, edge-based deployment, and hybrid deployment. Alternatively, the deployment module may not incorporate any methods at all.
[00057] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00058] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00059] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
[00060] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00061] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00062] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
 

Claims
I/We Claim:
1. A method for developing a predictive maintenance system for industrial equipment using machine learning algorithms, comprising: collecting data from sensors placed on the industrial equipment to monitor its performance and condition; preprocessing the collected data by removing noise, outliers, and missing values; feature engineering to extract relevant features from the preprocessed data; applying a machine learning algorithm to the extracted features to train a predictive model that can forecast future equipment failures; validating the accuracy and performance of the trained predictive model using test data; and deploying the predictive maintenance system on the industrial equipment to monitor its condition and predict equipment failures before they occur.
2. The method of claim 1, wherein the machine learning algorithm used to train the predictive model is a deep learning algorithm that employs neural networks to learn complex patterns and relationships in the collected data.
3. The method of claim 1, wherein the predictive maintenance system is integrated with a cloud-based platform that can remotely monitor the performance of the industrial equipment and provide real-time alerts and notifications to maintenance personnel.
4. A system for developing a predictive maintenance system for industrial equipment using machine learning algorithms, comprising: a data collection module configured to collect data from sensors placed on the industrial equipment to monitor its performance and condition; a data preprocessing module configured to preprocess the collected data by removing noise, outliers, and missing values; a feature engineering module configured to extract relevant features from the preprocessed data; a machine learning module configured to apply a machine learning algorithm to the extracted features to train a predictive model that can forecast future equipment failures; a validation module configured to validate the accuracy and performance of the trained predictive model using test data; and a deployment module configured to deploy the predictive maintenance system on the industrial equipment to monitor its condition and predict equipment failures before they occur.
5. The system of claim 4, wherein the data collection module comprises one or more sensors selected from the group consisting of temperature sensors, pressure sensors, vibration sensors, acoustic sensors, and current sensors.
6. The system of claim 4, wherein the data preprocessing module comprises one or more techniques selected from the group consisting of data cleaning, data normalization, data transformation, and data imputation.
7. The system of claim 4, wherein the feature engineering module comprises one or more techniques selected from the group consisting of statistical analysis, time-series analysis, Fourier analysis, wavelet analysis, and principal component analysis.
8. The system of claim 4, wherein the machine learning module comprises one or more algorithms selected from the group consisting of decision trees, random forests, support vector machines, artificial neural networks, and deep learning models.
9. The system of claim 4, wherein the validation module comprises one or more metrics selected from the group consisting of accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve, and mean squared error.
10. The system of claim 4, wherein the deployment module comprises one or more methods selected from the group consisting of cloud-based deployment, edge-based deployment, and hybrid deployment.

DEVELOPMENT OF A PREDICTIVE MAINTENANCE SYSTEM FOR INDUSTRIAL EQUIPMENT USING MACHINE LEARNING ALGORITHMS
Abstract
A method for developing a predictive maintenance system for industrial equipment using machine learning algorithms may be included in embodiments of the present disclosure. This method may include collecting data from sensors placed on the industrial equipment to monitor its performance and condition. In certain embodiments, there is additionally a step called preprocessing, which involves eliminating noise, outliers, and missing values from the data that was gathered. In certain embodiments, there is also the possibility of feature engineering, which is the process of extracting useful features from preprocessed data. In certain embodiments, there is also the possibility of using a machine learning algorithm on the extracted information in order to build a predictive model that may anticipate upcoming failures in equipment. Validating the correctness and performance of the trained prediction model by utilizing test data is another component that may be included in embodiments. In certain embodiments, it may also be possible to implement a predictive maintenance system on industrial equipment in order to monitor the status of such equipment and anticipate breakdowns in advance of their occurrence. , Claims:Claims
I/We Claim:
1. A method for developing a predictive maintenance system for industrial equipment using machine learning algorithms, comprising: collecting data from sensors placed on the industrial equipment to monitor its performance and condition; preprocessing the collected data by removing noise, outliers, and missing values; feature engineering to extract relevant features from the preprocessed data; applying a machine learning algorithm to the extracted features to train a predictive model that can forecast future equipment failures; validating the accuracy and performance of the trained predictive model using test data; and deploying the predictive maintenance system on the industrial equipment to monitor its condition and predict equipment failures before they occur.
2. The method of claim 1, wherein the machine learning algorithm used to train the predictive model is a deep learning algorithm that employs neural networks to learn complex patterns and relationships in the collected data.
3. The method of claim 1, wherein the predictive maintenance system is integrated with a cloud-based platform that can remotely monitor the performance of the industrial equipment and provide real-time alerts and notifications to maintenance personnel.
4. A system for developing a predictive maintenance system for industrial equipment using machine learning algorithms, comprising: a data collection module configured to collect data from sensors placed on the industrial equipment to monitor its performance and condition; a data preprocessing module configured to preprocess the collected data by removing noise, outliers, and missing values; a feature engineering module configured to extract relevant features from the preprocessed data; a machine learning module configured to apply a machine learning algorithm to the extracted features to train a predictive model that can forecast future equipment failures; a validation module configured to validate the accuracy and performance of the trained predictive model using test data; and a deployment module configured to deploy the predictive maintenance system on the industrial equipment to monitor its condition and predict equipment failures before they occur.
5. The system of claim 4, wherein the data collection module comprises one or more sensors selected from the group consisting of temperature sensors, pressure sensors, vibration sensors, acoustic sensors, and current sensors.
6. The system of claim 4, wherein the data preprocessing module comprises one or more techniques selected from the group consisting of data cleaning, data normalization, data transformation, and data imputation.
7. The system of claim 4, wherein the feature engineering module comprises one or more techniques selected from the group consisting of statistical analysis, time-series analysis, Fourier analysis, wavelet analysis, and principal component analysis.
8. The system of claim 4, wherein the machine learning module comprises one or more algorithms selected from the group consisting of decision trees, random forests, support vector machines, artificial neural networks, and deep learning models.
9. The system of claim 4, wherein the validation module comprises one or more metrics selected from the group consisting of accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve, and mean squared error.
10. The system of claim 4, wherein the deployment module comprises one or more methods selected from the group consisting of cloud-based deployment, edge-based deployment, and hybrid deployment.