Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to an inspection suite and particularly to a device for predictive maintenance of an industrial equipment.
Description of Related Art
[002] Failures in industrial machinery cause significant disruptions across manufacturing operations. Such events lead to sudden downtime that reduces productivity and increases repair expenses. Operational efficiency suffers when machinery stops unexpectedly, and businesses face cascading impacts on delivery schedules, customer commitments, and overall performance. Industries that rely on continuous production view unplanned machine failures as a critical obstacle to sustainable growth.
[003] Current industrial practices rely on methods such as manual inspections, rule-based alarm systems, and supervisory control and data acquisition (SCADA) alerts. These approaches focus on identifying obvious faults through periodic checks or preset thresholds. Organizations deploy these tools in an attempt to minimize downtime and keep equipment in working condition. In certain cases, patents and publications describe predictive maintenance frameworks, but commercial reliance continues on conventional monitoring through fixed alerts and human oversight.
[004] Such solutions remain inadequate for several reasons. Rule-based alarms lack adaptivity to evolving machine behavior and cannot capture subtle anomalies. Manual inspections consume time, depend on human judgment, and often fail to detect early signs of deterioration. SCADA alerts generate notifications based only on predefined parameters and provide limited context for decision-making. These limitations leave industries exposed to delayed fault recognition, recurring inefficiencies, and increased operational costs.
[005] There is thus a need for an improved and advanced device for predictive maintenance of an industrial equipment that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a device for predictive maintenance of an industrial equipment. The device comprising sensors adapted to collect operational data from the industrial equipment. The operational data is selected from a temperature, a vibration, a pressure, or a combination thereof. The system further comprising a processing unit communicatively connected to the sensors. The processing unit is configured to receive the operational data from the sensors; pre-process the received operational data to reduce noise and normalize values; relay the pre-processed operational data into a machine learning model configured to identify anomalies and predict potential failures of the industrial equipment; determine, based on an output of the machine learning model, a fault occurrence score in the industrial equipment; compare the fault occurrence score with a threshold magnitude; and generate a maintenance alert through a user interface, when the fault occurrence score is greater than the threshold magnitude, to enable a proactive maintenance action.
[007] Embodiments in accordance with the present invention further provide a method for predictive maintenance of an industrial equipment. The method comprising steps of receiving operational data from sensors; pre-processing the received operational data to reduce noise and normalize values; relaying the pre-processed operational data into a machine learning model configured to identify anomalies and predict potential failures of the industrial equipment; determining, based on an output of the machine learning model, a fault occurrence score in the industrial equipment; comparing the fault occurrence score with a threshold magnitude; and generating a maintenance alert through a user interface, when the fault occurrence score is greater than a threshold magnitude, to enable a proactive maintenance action.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a device for predictive maintenance of an industrial equipment.
[009] Next, embodiments of the present application may provide a device that processes real-time sensor data through advanced machine learning models to detect subtle anomalies in industrial machinery, enabling recognition of potential faults before they escalate into major failures.
[0010] Next, embodiments of the present application may provide a device that prevents unexpected breakdowns and minimizes operational interruptions.
[0011] Next, embodiments of the present application may provide a device that adapts to evolving equipment behavior, leading to precise fault predictions and fewer false positives or missed detections.
[0012] Next, embodiments of the present application may provide a device that allows operators to perform maintenance proactively and only when required.
[0013] Next, embodiments of the present application may provide a device that ensures machinery remains in optimal working condition, reducing wear and tear, that in turn extends the operational life of expensive industrial assets.
[0014] These and other advantages will be apparent from the present application of the embodiments described herein.
[0015] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0017] FIG. 1 illustrates a block diagram of a device for predictive maintenance of an industrial equipment, according to an embodiment of the present invention;
[0018] FIG. 2 illustrates a device for predictive maintenance of an industrial equipment, according to an embodiment of the present invention; and
[0019] FIG. 3 depicts a flowchart of a method for predictive maintenance of industrial equipment, according to an embodiment of the present invention.
[0020] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0021] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0022] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0023] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0024] FIG. 1 illustrates a block diagram of a device 100 for predictive maintenance of an industrial equipment 200 (as shown in FIG. 2), according to an embodiment of the present invention. In an embodiment of the present invention, the device 100 may be configured to operate as an intelligent unit for predictive maintenance of industrial equipment 200. The device 100 may acquire operational parameters, evaluate the parameters to identify abnormal conditions, and generate alerts to indicate potential failures. The device 100 may provide these alerts through an integrated interface, thereby enabling an operator to take proactive maintenance action and improve reliability of the industrial equipment 200. In an embodiment of the present invention, the novelty of the device 100 may lie in its seamless integration of Internet of Things (IoT) sensors with machine learning models. The integration may enable real-time inference that conventional systems fail to provide, thereby improving detection accuracy and responsiveness.
[0025] In an embodiment of the present invention, the device 100 may be self-learning and adaptive in nature. The device 100 may refine its parameters automatically as new operational data becomes available, thereby improving accuracy over time without requiring manual updates to preset thresholds. In an embodiment of the present invention, the device 100 may foster a proactive maintenance culture within industrial environments. By predicting failures before they occur, the device 100 may minimize unplanned downtime, thereby boosting productivity and reducing maintenance expenditure.
[0026] In an embodiment of the present invention, the device 100 may demonstrate a significant advantage over conventional rule-based or manual inspection systems. Unlike traditional systems that rely on static thresholds or operator judgment, the device 100 may dynamically learn from evolving operational data and provide reliable alerts with fewer false positives and negatives. In an embodiment of the present invention, the device 100 may utilize a variety of machine learning models, including but not limited to Long Short-Term Memory networks for temporal data prediction and Random Forest algorithms for anomaly classification. The availability of multiple models may ensure flexibility across different industrial scenarios.
[0027] In an embodiment of the present invention, the device 100 may be installed on various types of industrial machinery, such as motors, pumps, turbines, compressors, or conveyor systems. The device 100 may be mounted directly on the equipment housing or integrated within an existing monitoring setup. Placement of the device 100 may enable continuous observation of operational parameters at the source, thereby ensuring accurate detection of abnormal conditions and timely generation of maintenance alerts. For instance, the device 100 may be deployed on a factory conveyor motor. The device 100 may detect abnormal vibration patterns that usually precede bearing failure and may generate an alert before the motor stops. This may allow the operator to schedule maintenance during planned downtime, avoiding sudden breakdown and production loss.
[0028] According to the embodiments of the present invention, the device 100 may incorporate non-limiting hardware components to enhance a processing speed and an efficiency, such as the device 100 may comprise sensors 102, a processing unit 104, a user interface 106, and a communication interface 108. In an embodiment of the present invention, the hardware components of the device 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing devices.
[0029] In an embodiment of the present invention, the sensors 102 may be adapted to collect operational data from an industrial equipment 200. The sensors 102 may be high-precision Internet of Things (IoT) sensors. The operational data may be selected from a temperature, a vibration, a pressure, and so forth. In an embodiment of the present invention, the sensors 102 may be configured to operate as a distributed sensing layer within the device 100. The sensors 102 may be strategically positioned at critical regions of the industrial equipment 200, experiencing variations in stress, load, or environmental influence are most pronounced, thereby ensuring that the collected parameters accurately reflect the operational state of the equipment. The sensors 102 may be calibrated during installation to reduce error margins and maintain consistency across multiple equipment units. Recalibration of the sensors 102 may be conducted at defined intervals or upon detection of deviations from baseline conditions, thereby preserving long-term reliability of data acquisition.
[0030] In an embodiment of the present invention, the sensors 102 may provide redundancy by including multiple sensors of the same category at high-priority locations. This redundancy may enable cross-validation of data and minimize the impact of erroneous measurements caused by local disturbances, interference, or degradation of a single sensor. Through such redundancy, the sensors 102 may enhance fault tolerance, ensuring uninterrupted functionality of the device 100 even when one sensor fails.
[0031] In an embodiment of the present invention, the sensors 102 may further support adaptive sensitivity. The sensors within the set may adjust their resolution or sampling frequency when fluctuations in measured parameters approach critical limits. This adaptive adjustment may ensure that minor variations are captured with precision during abnormal events while avoiding unnecessary data overload during normal operation.
[0032] In an embodiment of the present invention, the sensors 102 may be configured to acquire data in a time-synchronized manner. Synchronization may be achieved through embedded clock control or through coordination with network-based timing protocols. Time-synchronized acquisition by the sensors 102 may allow accurate correlation between different parameters, enabling recognition of composite fault patterns that manifest across temperature, vibration, and pressure domains.
[0033] In an embodiment of the present invention, the processing unit 104 may be configured to receive the operational data from the sensors 102. The reception of data may occur through either wired connections, such as shielded industrial cables, or through wireless channels utilizing protocols including Wi-Fi, Zigbee, Bluetooth Low Energy, or other industrial IoT communication standards. The processing unit 104 may be configured to implement signal conditioning modules to ensure that the raw sensor signals are properly digitized and standardized for subsequent analysis. In an embodiment of the present invention, the processing unit 104 may be configured with input interfaces capable of handling multiple data streams simultaneously, thereby enabling concurrent reception of parameters such as temperature, vibration, and pressure. Buffering mechanisms within the processing unit 104 may be configured to temporarily store incoming data to prevent loss during high-volume transmission events, ensuring uninterrupted operation.
[0034] In an embodiment of the present invention, the processing unit 104 may be configured to incorporate data validation routines upon receipt of the operational data from the sensors 102. The validation may include verifying signal integrity, checking for missing or corrupted packets, and discarding anomalous readings that fall outside of the physically possible range for the monitored equipment. This ensures that only reliable data may be processed in subsequent stages. In an embodiment of the present invention, the processing unit 104 may be configured to maintain synchronization of received data from the sensors 102 by aligning timestamps associated with each data point. Such synchronization may allow the processing unit 104 to analyze correlations across multiple sensor modalities, thereby enabling accurate detection of abnormal events that manifest across temperature, vibration, and pressure domains simultaneously.
[0035] In an embodiment of the present invention, the processing unit 104 may be configured to pre-process the received operational data to reduce noise and normalize values. The pre-processing may be conducted using filtering, normalization, feature extraction, and so forth. The processing unit 104 may be configured to reduce noise in the received signals by applying digital filtering techniques such as low-pass filtering to eliminate high-frequency disturbances, high-pass filtering to suppress low-frequency drift, or band-pass filtering to isolate relevant frequency ranges. The processing unit 104 may further be configured to employ adaptive filtering methods that automatically adjust filter coefficients based on the statistical properties of the incoming data, thereby improving clarity in environments with variable interference. In an embodiment of the present invention, the processing unit 104 may be configured to normalize the operational data so that readings from different sensors and measurement scales are aligned to a common reference. The processing unit 104 may be configured to perform min-max scaling, z-score normalization, or mean-variance adjustment to bring heterogeneous sensor data into a standardized range, thereby reducing bias during subsequent analysis.
[0036] In an embodiment of the present invention, the processing unit 104 may be configured to extract features from the normalized data that are relevant for identifying equipment condition. The feature extraction performed by the processing unit 104 may include computation of time-domain indicators such as variance, skewness, or kurtosis, as well as frequency-domain indicators obtained through Fourier analysis. The processing unit 104 may further be configured to derive statistical summaries or spectral energy distributions that represent the underlying operational state of the equipment. In an embodiment of the present invention, the processing unit 104 may be configured to associate the pre-processed data with temporal markers so that features from different sensors are aligned in time. This synchronization may enable accurate correlation between temperature fluctuations, vibration spikes, and pressure deviations, thereby allowing precise identification of combined fault signatures.
[0037] In an embodiment of the present invention, the processing unit 104 may be configured to relay the pre-processed operational data into a machine learning model configured to identify anomalies and predict potential failures of the industrial equipment 200. The machine learning model may be, but not limited to, a Long Short-Term Memory network, a Random Forest algorithm, a Support Vector Machine, and so forth. While relaying, the processing unit 104 may be configured to format the pre-processed data into structured inputs that match the requirements of the selected model, such as vectors for time-series analysis or feature matrices for classification. In an embodiment of the present invention, the processing unit 104 may be configured to establish a data pipeline that transfers the normalized and feature-extracted parameters directly into memory accessible to the machine learning model. The relay of data may occur in real time, allowing the machine learning model to process continuous data streams without interruption. The processing unit 104 may further be configured to batch historical data segments for training or retraining cycles of the machine learning model, thereby enabling the system to adapt to evolving operational conditions.
[0038] In an embodiment of the present invention, the processing unit 104 may be configured to interface with different types of machine learning models depending on the industrial scenario. For instance, the processing unit 104 may be configured to provide sequential time-stamped data as input to the Long Short-Term Memory (LSTM) network, enabling recognition of temporal dependencies in vibration or pressure patterns. The processing unit 104 may be configured to relay multi-dimensional feature sets into the Random Forest algorithm for classification of normal versus abnormal operating states. Similarly, the processing unit 104 may be configured to provide linearly separable data into the Support Vector Machine (SVM) for precise detection of deviations from baseline operation. In an embodiment of the present invention, the processing unit 104 may be configured to monitor the success of data relay by implementing acknowledgment protocols, error correction techniques, and redundancy checks. This ensures that the machine learning model receives accurate and complete data for anomaly detection and failure prediction. By maintaining reliable data transfer, the processing unit 104 may enable the machine learning model to operate effectively in real-time industrial environments.
[0039] In an embodiment of the present invention, the processing unit 104 may be configured to determine, based on an output of the machine learning model, a fault occurrence score in the industrial equipment 200. The fault occurrence score may represent a quantitative measure of the likelihood that a fault or abnormal condition exists or may be likely to occur within a specific timeframe. In an embodiment of the present invention, the processing unit 104 may be configured to compute the fault occurrence score by interpreting the probabilistic or classification output generated by the machine learning model. For example, when the machine learning model produces class probabilities indicating normal versus abnormal conditions, the processing unit 104 may assign a score proportional to the probability of abnormality. Similarly, when the machine learning model provides anomaly scores or regression outputs, the processing unit 104 may normalize these values to a unified scale for consistent interpretation.
[0040] In an embodiment of the present invention, the processing unit 104 may be configured to aggregate fault indicators across multiple sensor modalities to compute a comprehensive fault occurrence score. For example, the processing unit 104 may combine weighted contributions from vibration, temperature, and pressure data, with the weightage determined based on the historical relevance of each parameter to particular types of equipment failures. Such aggregation may provide a holistic measure of equipment health. In an embodiment of the present invention, the processing unit 104 may be configured to dynamically adjust the fault occurrence score computation based on operating conditions or recent maintenance history. For instance, if the equipment has recently undergone servicing, the processing unit 104 may reduce the influence of transient anomalies to prevent false alarms. This adaptability may allow the fault occurrence score to accurately reflect the true risk of failure in real-world conditions. In an embodiment of the present invention, the processing unit 104 may be configured to output the fault occurrence score as a numerical value, percentage likelihood, or categorical indicator that may be displayed on the user interface 106. This score may then serve as the basis for threshold comparison, enabling proactive maintenance actions.
[0041] In an embodiment of the present invention, the processing unit 104 may be configured to compare the fault occurrence score with a threshold magnitude. The threshold magnitude may be defined as a baseline risk limit that distinguishes normal operating conditions from conditions requiring attention. In an embodiment of the present invention, the processing unit 104 may be configured to store one or more threshold magnitudes in an internal memory. These thresholds may be predetermined by system designers, configured manually by operators, or dynamically adapted by learning algorithms based on historical maintenance data. The processing unit 104 may retrieve the relevant threshold magnitude corresponding to the type of parameter or equipment under analysis before performing the comparison.
[0042] In an embodiment of the present invention, the processing unit 104 may be configured to perform the comparison using numerical evaluation. The fault occurrence score may be checked against the threshold value. If the score may be less than or equal to the threshold, the processing unit 104 may classify the equipment status as normal. If the score exceeds the threshold, the processing unit 104 may classify the equipment status as abnormal and prepare to generate a maintenance alert. In an embodiment of the present invention, the processing unit 104 may be configured to apply multiple threshold magnitudes that correspond to different levels of severity. For example, a lower threshold may correspond to an early warning level, while a higher threshold may indicate critical failure risk. The comparison may therefore provide graded results, enabling alerts with varying levels of urgency. In an embodiment of the present invention, the processing unit 104 may be configured to log the results of the threshold comparison into system memory for auditing and performance tracking. This logged information may later be used to refine the threshold magnitudes or to validate the accuracy of the fault occurrence score calculation.
[0043] In an embodiment of the present invention, the processing unit 104 may be configured to generate a maintenance alert through the user interface 106, when the fault occurrence score may be greater than the threshold magnitude, to enable a proactive maintenance action. The threshold magnitude may be predefined by a system administrator, dynamically set by adaptive learning algorithms, or adjusted based on historical equipment behaviour. When the computed fault occurrence score crosses this magnitude, the processing unit 104 may initiate alert generation. In an embodiment of the present invention, the processing unit 104 may be configured to compose the maintenance alert as a structured message containing details of the abnormal condition. Such details may include the specific sensor readings that contributed to the fault occurrence score, the predicted failure type, and the urgency level. The processing unit 104 may further be configured to assign a severity classification, such as “warning,” “critical,” or “immediate action required,” based on the magnitude of the fault occurrence score relative to the threshold.
[0044] In an embodiment of the present invention, the processing unit 104 may be configured to display the maintenance alert through the user interface 106 in multiple formats. The alert may include textual notifications, graphical indicators such as color-coded severity bars, or trend charts that highlight the evolution of operational parameters leading up to the alert condition. This visualization may enable the operator to quickly assess the context of the fault and decide on corrective measures. In an embodiment of the present invention, the processing unit 104 may be configured to support escalation protocols in the maintenance alert. For example, if the score greatly exceeds the threshold, the processing unit 104 may trigger additional actions such as sending notifications to remote terminals, logging the event for compliance purposes, or interfacing with an automated shutdown system to prevent catastrophic equipment damage. In an embodiment of the present invention, the processing unit 104 may be configured to ensure that the maintenance alert may be generated with minimal latency, so that proactive maintenance action can be undertaken before a fault leads to unplanned downtime. This proactive mechanism may improve operational reliability and extend the lifecycle of the industrial equipment 200.
[0045] In an embodiment of the present invention, the user interface 106 may be adapted to receive the maintenance alert generated by the processing unit 104. The maintenance alert may comprise displaying a graphical trend analysis of the operational data on the user interface 106. The maintenance alert may comprise a severity level associated with the predicted failure. The user interface 106 may be adapted to provide a real-time visualization of equipment health status of the industrial equipment 200.
[0046] In an embodiment of the present invention, the user interface 106 may include a dashboard configured to present alerts along with visual aids such as graphs and trend analyses of operational parameters. The dashboard may provide a comprehensive view of equipment health, enabling operators to interpret long-term patterns in addition to immediate alerts. In an embodiment of the present invention, the user interface 106 may be configured to provide actionable insights in addition to maintenance alerts. The actionable insights may include recommendations for scheduling, prioritization of components requiring attention, or predictive indicators that allow an operator to plan intervention without disrupting production schedules.
[0047] In an embodiment of the present invention, the communication interface 108 may be adapted to transmit the generated maintenance alert to a remote device 202 (as shown in FIG. 2). The communication interface 108 may support both wired and wireless connectivity options to ensure reliable delivery of alerts under diverse industrial conditions. In an embodiment of the present invention, the communication interface 108 may be adapted to utilize wireless protocols such as Wi-Fi, Zigbee, Bluetooth Low Energy, or LoRaWAN for short- and medium-range communication within an industrial environment. The communication interface 108 may further be configured to support cellular standards such as 4G LTE or 5G for long-range communication, thereby enabling transmission of the generated maintenance alert to the remote device 202.
[0048] In an embodiment of the present invention, the communication interface 108 may be adapted to integrate with cloud-based platforms and Internet of Things (IoT) ecosystems. For example, the communication interface 108 may connect with Amazon Web Services IoT Core or Microsoft Azure IoT Hub, thereby allowing the maintenance alert to be stored, visualized, and acted upon at a centralized dashboard. The communication interface 108 may further integrate with consumer platforms such as Amazon Alexa or Google Assistant, enabling voice-enabled notifications that inform operators or supervisors of impending equipment issues in real time.
[0049] In an embodiment of the present invention, the communication interface 108 may be adapted to secure the transmission of the generated maintenance alert to the remote device 202 using encryption protocols such as Transport Layer Security (TLS) or Virtual Private Networks (VPNs). Such measures may ensure that only authenticated remote device 202 are able to access and interpret the generated maintenance alert. In an embodiment of the present invention, the communication interface 108 may be adapted to transmit the generated maintenance alert to the remote device 202 in multiple formats, including text notifications, emails, push alerts, or voice announcements. For instance, the communication interface 108 may send the generated maintenance alert as a Short Message Service (SMS) notification to a technician’s mobile phone while simultaneously delivering the same generated maintenance alert to the remote device 202. In an embodiment of the present invention, the communication interface 108 may be adapted to ensure redundancy by transmitting the generated maintenance alert to the remote device 202 through multiple parallel channels, such as both Zigbee and Wi-Fi, thereby guaranteeing reliable delivery even in case of individual network failure.
[0050] FIG. 2 illustrates the device 100, according to an embodiment of the present invention. In an embodiment of the present invention, the industrial equipment 200 may be any machine, apparatus, or system used in a production or processing environment where continuous operation may be critical. The industrial equipment 200 may include, but may be not limited to, motors, pumps, compressors, turbines, conveyor systems, or hydraulic units. The industrial equipment 200 may be subject to operational stresses such as mechanical wear, thermal loading, or pressure fluctuations that, if left undetected, may result in unexpected breakdowns. The industrial equipment 200 may therefore serve as the host system, accommodating the device 100 may be deployed for predictive maintenance.
[0051] In an embodiment of the present invention, the remote device 202 may be configured to receive the generated maintenance alert transmitted by the communication interface 108 of the device 100. The remote device 202 may be a handheld mobile device, a tablet, a desktop computer, or a supervisory control station equipped with the ability to display alerts and notifications. In certain embodiments, the remote device 202 may be a cloud-connected endpoint integrated with platforms such as Amazon Web Services, Microsoft Azure, or Google Cloud IoT, enabling centralized monitoring of multiple industrial equipment 200. In consumer-level integration, the remote device 202 may further include smart assistants such as Amazon Alexa or Google Assistant, that may provide voice-based alerts to the operator. The remote device 202 may therefore act as an interface between the device 100 and the operator, enabling proactive maintenance actions to be taken without requiring physical presence near the industrial equipment 200.
[0052] In an exemplary embodiment of the present invention, the industrial equipment 200 may be a motor deployed in a conveyor system of a manufacturing unit. The device 100 may be mounted on the casing of the industrial equipment 200 to observe operational parameters during continuous operation. The sensors 102 within the device 100 may capture temperature variations and vibration levels in real time, particularly around the stator region of the industrial equipment 200. The processing unit 104 of the device 100 may be adapted to pre-process the data received from the sensors 102 and may relay the refined data into a machine learning model trained to detect winding-related faults. The machine learning model may recognize patterns such as abnormal temperature rise in the stator or irregular vibration frequencies that are characteristic of insulation breakdown in motor windings. Based on the analysis, the processing unit 104 may determine a fault occurrence score that indicates a high probability of winding malfunction in the industrial equipment 200.
[0053] When the fault occurrence score exceeds a defined threshold magnitude, the processing unit 104 may be configured to generate a maintenance alert. The user interface 106 of the device 100 may present the alert in a graphical format, highlighting the rising temperature trend and abnormal vibration spikes near the winding. The communication interface 108 of the device 100 may further be adapted to transmit the generated maintenance alert to a remote device 202, such as a technician’s smartphone or a centralized monitoring dashboard. The remote device 202 may display the alert details, including the location of the anomaly and the projected risk of winding failure. On receiving the generated maintenance alert, the operator may proactively schedule servicing of the motor, such as insulation testing or rewinding, before the fault escalates into a complete breakdown. This proactive intervention may prevent costly downtime of the conveyor system and extend the operational lifespan of the industrial equipment 200.
[0054] FIG. 3 depicts a flowchart of a method 300 for predictive maintenance of the industrial equipment 200 using the device 100, according to an embodiment of the present invention.
[0055] At step 302, the device 100 may receive the operational data from the sensors 102.
[0056] At step 304, the device 100 may pre-process the received operational data to reduce the noise and normalize the values.
[0057] At step 306, the device 100 may relay the pre-processed operational data into the machine learning model configured to identify the anomalies and predict the potential failures of the industrial equipment 200.
[0058] At step 308, the device 100 may determine, based on the output of the machine learning model, the fault occurrence score in the industrial equipment 200.
[0059] At step 310, the device 100 may compare the fault occurrence score with the threshold magnitude. Upon comparison, if the fault occurrence score may be greater than the threshold magnitude, then the method 300 may proceed to a step 312. Else, the method 300 may revert to the step 302.
[0060] At step 312, the device 100 may generate the maintenance alert through the user interface 106.
[0061] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0062] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A device (100) for predictive maintenance of an industrial equipment (200), the device (100) comprising:
sensors (102) adapted to collect operational data from the industrial equipment (200), wherein the operational data is selected from a temperature, a vibration, a pressure, or a combination thereof; and
a processing unit (104) communicatively connected to the sensors (102), characterized in that the processing unit (104) is configured to:
receive the operational data from the sensors (102);
pre-process the received operational data to reduce noise and normalize values;
relay the pre-processed operational data into a machine learning model configured to identify anomalies and predict potential failures of the industrial equipment (200);
determine, based on an output of the machine learning model, a fault occurrence score in the industrial equipment (200);
compare the fault occurrence score with a threshold magnitude; and
generate a maintenance alert through a user interface (106), when the fault occurrence score is greater than the threshold magnitude, to enable a proactive maintenance action.
2. The device (100) as claimed in claim 1, wherein the sensors (102) are high-precision Internet of Things (IoT) sensors.
3. The device (100) as claimed in claim 1, wherein the processing unit (104) is configured to conduct pre-processing using filtering, normalization, feature extraction, or a combination thereof.
4. The device (100) as claimed in claim 1, wherein the machine learning model is selected from a Long Short-Term Memory network, a Random Forest algorithm, a Support Vector Machine, or a combination thereof.
5. The device (100) as claimed in claim 1, wherein the maintenance alert comprises displaying a graphical trend analysis of the operational data on the user interface (106).
6. The device (100) as claimed in claim 1, wherein the maintenance alert comprises a severity level associated with the predicted failure.
7. The device (100) as claimed in claim 1, wherein the user interface (106) provides a real-time visualization of equipment health status.
8. The device (100) as claimed in claim 1, wherein the processing unit (104) is configured to transmit the maintenance alert to a remote device (202) through a communication interface (108).
9. The device (100) as claimed in claim 1, wherein the processing unit (104) is configured to enable the machine learning model to self-learn and update parameters based on incoming operational data.
10. A method (300) for predictive maintenance of an industrial equipment (200), the method (300) characterized by steps of:
receiving operational data from sensors (102);
pre-processing the received operational data to reduce noise and normalize values;
relaying the pre-processed operational data into a machine learning model configured to identify anomalies and predict potential failures of the industrial equipment (200);
determining, based on an output of the machine learning model, a fault occurrence score in the industrial equipment (200);
comparing the fault occurrence score with a threshold magnitude; and
generating a maintenance alert through a user interface (106), when the fault occurrence score is greater than a threshold magnitude, to enable a proactive maintenance action.

Date: October 08, 2025
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant