Description:TECHNICAL FIELD
[0001] The present disclosure relates to motor and pump, specifically a motor and pump system that capable of monitoring operational status of each individual component and providing real-time status updates.

BACKGROUND
[0002] Background description includes information that may be useful in understanding the present disclosure. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosure, or that any publication specifically or implicitly referenced is prior art.
[0003] Conventional motor and pump systems often lack capability to independently monitor the performance of their individual components. This limitation makes it difficult to detect early signs of malfunction or inefficiency, leading to increased maintenance costs, unplanned downtime, and reduced operational reliability. Traditional systems rely on periodic manual inspections or external sensors to assess performance, which is often inefficient and prone to delays in fault detection.
[0004] Existing solutions attempt to address these challenges using variable frequency drives (VFDs), basic electronic controllers, or external monitoring devices. While VFDs offer some level of motor speed control, they do not provide detailed insights into the condition of each component within the system. Similarly, sensor-based monitoring solutions are often limited in scope, focusing on system performance rather than individual component health. These solutions typically lack the ability to dynamically adjust operations in response to detected deviations, making fault detection and maintenance reactive rather than proactive.
[0005] A major drawback of current systems is the inability to precisely identify which specific component such as the motor, pump, bearings, or impeller is underperforming or failing. Without this level of detail, maintenance teams must conduct time-consuming inspections or replace entire units unnecessarily. Furthermore, existing monitoring techniques often fail to provide real-time alerts, leading to prolonged periods of suboptimal operation and potential equipment damage. Additionally, most traditional motor and pump systems do not leverage predictive analytics, which can help anticipate failures before they occur and improve long-term system efficiency.
[0006] There is, therefore, a need for a solution that continuously monitors working condition of each component and provides timely updates.

OBJECTS OF THE PRESENT DISCLOSURE
[0007] An object of the present disclosure is to provide a solution that continuously monitors operational status of each individual component in real-time.
[0008] An object of the present disclosure is to provide a solution that detects deviations from expected performance and classifies faults based on severity for effective diagnostics.
[0009] An object of the present disclosure is to provide a solution that predicts deviations before they lead to component failure.
[0010] An object of the present disclosure is to provide a solution that transmits real-time alerts upon detecting deviations or faults.
[0011] An object of the present disclosure is to provide a solution that optimizes power consumption by adjusting motor and pump performance according to required flow rate.
[0012] An object of the present disclosure is to provide a solution that allows dynamic adjustment of operational parameters based on inputs received through wired and wireless interfaces.
[0013] An object of the present disclosure is to provide a solution that ensures smooth transitions between different flow rate levels to prevent abrupt operational changes.
[0014] An object of the present disclosure is to provide a solution that reduces mechanical wear and tear by scaling down performance when full capacity is not required.
[0015] An object of the present disclosure is to provide a solution that enables remote monitoring and real-time data logging.
[0016] An object of the present disclosure is to provide a solution that enhances reliability, minimizes downtime, and extends component lifespan through predictive maintenance strategies.

SUMMARY
[0017] Aspects of the present disclosure relate to motor and pump, specifically a motor and pump system that is capable of monitoring the operational status of each individual component and providing real-time status updates. The proposed system utilizes signal transmission units to assess working condition of components such as the motor, pump, bearings, and impeller, enabling early fault detection, real-time diagnostics, and predictive maintenance. In addition, ensures proactive identification of deviations or malfunctions, enhancing operational efficiency, reducing downtime, and optimizing maintenance processes.
[0018] An aspect of the present disclosure pertains to a motor and pump system capable of monitoring the operational status of each individual component and transmitting real-time status updates. The system includes a motor and pump, each comprising multiple components, and a signal transmission unit operatively coupled to each component. The signal transmission unit generates and transmits signals indicative of the operational status of the respective component to a control unit. The control unit is communicably coupled to the signal transmission unit and a memory storing processor-executable instructions. Upon execution, the control unit extracts features from the received signals, compares them with pre-stored threshold values, detects deviations based on the comparison, and generates an alert when a deviation is identified. The generated alert is transmitted to a computing device for further analysis or maintenance actions. The system is capable of classifying deviations into predefined fault categories and determining their severity based on deviation magnitude and historical failure patterns.
[0019] In addition, the control unit dynamically adjusts pre-stored threshold values based on real-time performance metrics such as operational conditions, environmental factors, historical performance data, and detected deviations. Machine learning algorithms may be applied to analyze historical signal data, predict potential deviations, and optimize system performance.
[0020] In an aspect, the system receives inputs through wired and wireless interfaces, allowing for dynamic adjustment of operational parameters based on set flow rates or other defined parameters. At the highest flow rate, the control unit commands all components to function at full capacity and scales down performance as an operator reduces the flow rate. The system optimizes power consumption by proportionally adjusting motor and pump performance to the required flow rate.
[0021] In an aspect, the system is capable of triggering corrective responses such as adjusting operational parameters, initiating a controlled shutdown, or scheduling maintenance upon detecting deviations. The control unit is communicably coupled to a server, enabling remote monitoring, data logging, and real-time updates.
[0022] In an aspect, by continuously monitoring the condition of each component, providing real-time diagnostics, and integrating predictive maintenance strategies, the intelligent motor and pump system enhances operational efficiency, reduces downtime, minimizes maintenance costs, and extends the lifespan of components.
[0023] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0024] The following drawings form part of the present specification and are included to further illustrate aspects of the present disclosure. The disclosure may be better understood by reference to the drawings in combination with the detailed description of the specific embodiments presented herein.
[0025] FIG. 1 illustrates an exemplary block diagram of proposed motor and pump system, in accordance with an embodiment of the present disclosure.
[0026] FIG. 2 illustrates an exemplary flow chart to illustrate working of proposed motor and pump system, in accordance with an embodiment of the present disclosure.
[0027] FIG. 3 illustrates an exemplary flow chart to illustrate working of proposed motor and pump system, upon receiving commands from an operator, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0028] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0029] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0030] Embodiments of the present disclosure relate to motor and pump, specifically a motor and pump system that is capable of monitoring the operational status of each individual component and providing real-time status updates.
[0031] An embodiment of the present disclosure pertains to a motor and pump system capable of monitoring the operational status of each individual component and transmitting real-time status updates. The system includes a motor and pump, each comprising multiple components, and a signal transmission unit operatively coupled to each component. The signal transmission unit generates and transmits signals indicative of the operational status of the respective component to a control unit. The control unit is communicably coupled to the signal transmission unit and a memory storing processor-executable instructions. Upon execution, the control unit extracts features from the received signals, compares them with pre-stored threshold values, detects deviations based on the comparison, and generates an alert when a deviation is identified. The generated alert is transmitted to a computing device for further analysis or maintenance actions. The system is capable of classifying deviations into predefined fault categories and determining their severity based on deviation magnitude and historical failure patterns.
[0032] In addition, the control unit dynamically adjusts pre-stored threshold values based on real-time performance metrics such as operational conditions, environmental factors, historical performance data, and detected deviations. Machine learning algorithms may be applied to analyze historical signal data, predict potential deviations, and optimize system performance.
[0033] In an embodiment, the system receives inputs through wired and wireless interfaces, allowing for dynamic adjustment of operational parameters based on set flow rates or other defined parameters. At the highest flow rate, the control unit commands all components to function at full capacity and scales down performance as an operator reduces the flow rate. The system optimizes power consumption by proportionally adjusting motor and pump performance to the required flow rate.
[0034] In an embodiment, the system is capable of triggering corrective responses such as adjusting operational parameters, initiating a controlled shutdown, or scheduling maintenance upon detecting deviations. The control unit is communicably coupled to a server, enabling remote monitoring, data logging, and real-time updates.
[0035] In an embodiment, by continuously monitoring the condition of each component, providing real-time diagnostics, and integrating predictive maintenance strategies, the intelligent motor and pump system enhances operational efficiency, reduces downtime, minimizes maintenance costs, and extends the lifespan of components.
[0036] FIG. 1, illustrates an exemplary block diagram of proposed motor and pump system (100) (interchangeably referred to as system (100), hereinafter). The system (100) includes a motor (102) and a pump (104), each comprising a plurality of components (not shown) including but not limited to stator, rotor, bearings, impeller, shaft, casing, and seals. The system also includes a signal transmission unit (106) operatively coupled with each component of the motor (102) and the pump (104). This signal transmission unit (106) is configured for monitoring operational conditions of each individual component by generating signals that reflect their performance. The operational conditions of each component are determined based on at least one: rotational speed, torque, power consumption, temperature, vibration, pressure, or fluid flow rate.
[0037] In an exemplary embodiment, operational condition of rotor of the motor (102) may be determined by monitoring its rotational speed and vibration. If the rotational speed is lower than expected, it may indicate a mechanical issue such as a worn bearing or a blockage in the pump, while unusual vibration patterns can suggest an imbalance or misalignment of the rotor. Similarly, the temperature of impeller of the pump (104) may be monitored, as an increase in temperature may signal friction due to insufficient lubrication or a failure in cooling system. Additionally, pressure readings from the pump may help determine whether it is operating at its intended capacity or if there is a blockage or leak affecting performance.
[0038] In an embodiment, the system (100) includes a control unit (108) communicably coupled to the signal transmission unit (106). The control unit (108) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the control unit (108) may be configured to fetch and execute computer-readable instructions stored in a memory (110) of the control unit (108). The memory (110) may store one or more computer-readable instructions or routines, which may be fetched and executed while controlling components of the system (100). The memory (110) may include any non-transitory storage device including, for example, volatile memory such as Random Access Memory (RAM), or non-volatile memory such as an Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.
[0039] The control unit (108) is communicatively coupled to a computing device (112) through a communication unit (120) to receive commands to control components of the system (100). This computing device (112) may be a smartphone, tablet, computer, or any similar device capable of hosting a web client or application to facilitate communication and interaction. The communication unit (120) may be wired communication means, or wireless communication means, or a combination thereof. In some embodiments, the wired communication means may include, but not limited to, wires, cables, data buses, optical fibre cables, and the like.
[0040] In some embodiments, the wireless communication means may include, but not be limited to, telecommunication networks, Near Field Communication (NFC), Bluetooth, Internet, Local Area Networks (LAN), Wide Area Networks (WAN), Light Fidelity (Li-FI) networks, a carrier network, and the like. In some embodiments, the form factor of the data transmitted through the communication means may be any one or combination of including, but not limited to, analogue signals, electrical signals, digital signals, radio signals, infrared signals, data packets, and the like.
[0041] In an embodiment, the control unit (108) is configured for extracting features from the signals received from the signal transmission unit (106). These features include characteristics such as amplitude, frequency, phase, waveform pattern, signal distortion, noise levels, and duty cycle variations. The control unit (108) applies signal processing techniques to isolate these features from the received signals. Once extracted, the control unit (108) compares the features with pre-stored threshold values stored in the memory (110). The comparison helps identify if the current performance of the components deviates from acceptable limits.
[0042] In addition, pattern recognition techniques are further used to analyze the extracted features and detect any early-stage anomalies that may indicate potential failure of the respective components. If a deviation from the expected performance is detected, an alert is generated. This alert is further transmitted to the computing device (110), allowing for real-time monitoring and intervention. Additionally, the control unit (108) classifies the detected deviations into predefined fault categories, determining severity of each fault based on the magnitude of the deviation and historical failure patterns.
[0043] When deviations are detected, the system (100) triggers a corrective response. This response may include adjusting operational parameters, initiating a controlled shutdown to prevent further damage, or scheduling maintenance for the affected components. These actions help to address issues proactively, ensuring the system operates efficiently and minimizing the risk of unexpected failures.
[0044] In an embodiment, the control unit (108) is configured to extract features from the signals received from the signal transmission unit (106). These features include characteristics such as amplitude, frequency, phase, waveform pattern, signal distortion, noise levels, and duty cycle variations. The control unit applies signal processing techniques to isolate these features from the received signals. Once extracted, the control unit (108) compares the features with pre-stored threshold values stored in memory. The comparison helps identify if the current performance of the components deviates from acceptable limits.
[0045] In addition, pattern recognition techniques are further used to analyze the extracted features and detect any early-stage anomalies that may indicate potential failure of the respective components. If a deviation from the expected performance is detected, an alert is generated. This alert is further transmitted to the computing device (110), allowing for real-time monitoring and intervention.
[0046] Additionally, the control unit (108) classifies the detected deviations into predefined fault categories, determining severity of each fault based on magnitude of deviation and historical failure patterns. For instance, the system (100) might classify faults into categories such as "minor," "moderate," or "critical" depending on how far the deviation exceeds predefined threshold values. A small deviation in signal amplitude might be categorized as "minor," while a large deviation in frequency can be classified as "critical." This classification helps to prioritize which faults need immediate attention and which can be dealt with at a later time.
[0047] The severity of each fault is further determined by magnitude of deviation. A larger deviation from the expected signal parameters suggests a more significant issue that can lead to component failure. For instance, a sudden drop in the rotational speed of the motor can indicate serious mechanical wear, while a minor fluctuation in temperature might only suggest a minor issue.
[0048] The control unit also compares the detected deviations with historical failure patterns. By looking at how previous faults developed and their outcomes, the control unit can assess whether the current deviation is part of a pattern that might predict future failure. This historical analysis improves ability of the system (100) to predict and prevent issues before they lead to critical failures, enhancing reliability and operational efficiency.
[0049] Further, when deviations are detected, the system (100) triggers a corrective response. This response may involve adjusting operational parameters, initiating a controlled shutdown to prevent further damage, or scheduling maintenance for the affected components. These actions help to address issues proactively, ensuring the system (100) operates efficiently and minimizing risk of unexpected failures.
[0050] For instance, if the system (100) detects a deviation in rotational speed of the motor (102), which falls below the acceptable threshold, the control unit may trigger a corrective response. If the deviation is minor, the system (100) can automatically adjust operational parameters of the motor (102), such as increasing the power supplied to the motor to compensate for the reduced speed. However, if the deviation is more significant, indicating a potential risk of damage, the system (100) may initiate a controlled shutdown. This shutdown would safely stop the motor and pump to prevent further damage, allowing for investigation and repairs. In another scenario, if the deviation suggests wear on a specific component, the system might schedule maintenance to inspect or replace the affected part, ensuring the system (100) continues to run smoothly and minimizing the chance of an unexpected failure. These corrective responses help maintain operational efficiency and reduce the risk of unplanned downtime.
[0051] In an embodiment, the control unit (108) is further configured to dynamically adjust the pre-stored threshold values based on real-time performance metrics of the respective components. These threshold values, which are predefined limits used to assess normal operation, can be adjusted in real-time to reflect the current operating conditions of the system. For example, if the motor is running under high load conditions, the threshold values for parameters such as temperature, rotational speed, or power consumption may be adjusted to higher limits to accommodate the increased load without triggering false alarms. The control unit (108) evaluates real-time performance metrics based on factors such as the operational conditions (e.g., speed, torque), environmental factors (e.g., temperature, humidity), historical performance data (e.g., past performance trends), and detected deviations (e.g., anomalies that have been observed recently). By considering these factors, the control unit (108) can ensure that the threshold values are always aligned with current operating state of the system (100), improving accuracy and preventing unnecessary shutdowns or alerts.
[0052] Additionally, the control unit (108) applies machine learning techniques to analyze the historical performance data stored in the memory (110). This historical data includes past operational records, faults, and any other relevant performance indicators. By using machine learning algorithms, the control unit (108) can identify patterns in data that may indicate potential failures or performance issues. For instance, if a specific pattern in performance of the motor (102) precedes a failure in a component, the system (100) can learn to recognize this pattern and predict that the component is likely to fail in the near future. This allows the system to predict deviations or faults prior to occurrence, giving operators a chance to address issues before they lead to downtime or damage. By incorporating machine learning, the system (100) becomes increasingly accurate and efficient at predicting potential failures, enhancing both reliability and operational longevity.
[0053] In an embodiment, the control unit (108) to interact with an operator through a graphical user interface (GUI) displayed on the computing device (112). The GUI allows the user to input commands or adjustments related to operation of the system (100). For example, a user can specify the desired flow rate through the GUI, and the control unit will adjust parameters of the system (100) accordingly. Once the user inputs desired flow rate, the control unit (108) dynamically adjusts the motor speed, pump pressure, and valve positioning in real-time to match set flow rate. The motor speed may be increased or decreased to achieve the required flow rate, the pump pressure may be adjusted to ensure efficient fluid movement, and the valves may open or close to regulate the flow within the system. The control unit constantly monitors these components and makes the necessary adjustments to maintain the desired flow rate, ensuring that the system performs optimally.
[0054] Additionally, the control unit (108) is configured to adjust the power consumption of the motor (102) and the pump (104) proportionally to the set flow rate. When the operator sets a higher flow rate, the system requires more power, so the control unit will increase the power supplied to the motor and pump to meet the demand. Conversely, if the flow rate is reduced, the system will lower the power consumption to match the reduced demand. This adjustment helps maintain efficiency, ensuring that the system (100) does not use more energy than necessary while still meeting the flow rate requirements.
[0055] For instance, the user interacts with the GUI on the computing device to set a flow rate of 50 liters per minute. In response, the control unit (108) dynamically adjusts the motor speed to achieve the required flow, increases the pump pressure to maintain fluid movement, and adjusts the valve positioning to regulate the flow rate. At the same time, the control unit (108) ensures that the motor and power consumption of the pump (104) is proportional to the set flow rate. If the flow rate is increased to 100 liters per minute, the system (100) can adjust the motor speed and pump pressure accordingly, while increasing the power consumption to meet new demand.
[0056] Through this connection, the control unit (108) continuously transmits operational data and performance metrics such as motor speed, pump pressure, temperature, and other relevant parameters to the server (122). This real-time data can be monitored, analyzed, and stored on the server for further review or decision-making. By being communicably coupled to the server (122) through the communication unit (120), the control unit (108) allows for centralized monitoring of performance of the system (100), even if the system (100 is physically distant from the server (122). This enables users or operators to track operational health of the motor and pump, detect any anomalies, and perform diagnostics remotely, without needing to be on-site. Additionally, historical data and performance trends from multiple systems (100) can be stored on the server (122) for future analysis, allowing for better maintenance planning and predictive analysis.
[0057] Referring to FIG. 2 an exemplary flow chart (200) to illustrate working of the proposed system (100) is disclosed.
[0058] At step (202), the system (100) is powered on and initialized. At step (204), the control unit (108) establishes communication with the signal transmission unit (106) for each component of the motor (102) and pump (104). Continuing further, at step (206), the signal transmission unit (106) transmits operational data, such as motor speed, pump pressure, temperature, and other relevant parameters to the control unit (108). Continuing further, at step (208), the control unit (108) receives the signals and extracts relevant features such as amplitude, frequency, noise levels, and more. At step (210), the control unit (108) applies signal processing techniques to analyze the received signals.
[0059] Continuing further, at step (212), the extracted features are compared with pre-stored threshold values in the memory (110). Continuing further, at step (214), the control unit (108) detects deviations in the operational status of the motor (102) or pump (104) based on the comparison of the extracted features with the threshold values. At step (216), the control unit (108) classifies the detected deviations into predefined fault categories and determines the severity of each fault based on deviation magnitude and historical failure patterns.
[0060] Continuing further, at step (218), the control unit (108) generates the alert upon detecting a deviation, and at step (220), the generated alert is transmitted to a computing device (112), notifying the user of the detected deviation or fault. Continuing further, at step (222), based on the detected deviation, the system triggers a corrective response, which may include adjusting operational parameters, initiating a controlled shutdown, or scheduling maintenance.
[0061] Referring to FIG. 3 an exemplary flow chart (300) to illustrate working of the proposed system (100) upon receiving commands from an operator is disclosed.
[0062] At step (302), the operator inputs desired flow rate or operational adjustment via the graphical user interface (GUI) on the computing device (112). Continuing further, at step (304), the control unit (108) dynamically adjusts the motor speed, pump pressure, and valve positioning in real-time based on the received user input for flow rate adjustment. Continuing further, at step (306) involves the control unit adjusting the power consumption of the motor (102) and pump (104) proportionally to the set flow rate.
[0063] Continuing further, at step (308), the control unit (108) transmits real-time data from the system (100) to the server (122) for monitoring and logging. In step (310), the server (122) receives data from multiple systems (100) connected to it, allowing centralized monitoring. Continuing further, at step (312) involves the server analyzing the real-time data from multiple systems (100) to monitor system performance and identify potential issues. Continuing further, at step (314), the server may use historical performance data and machine learning techniques to predict potential deviations or failures across the connected systems (100), and at step (316), the system continues to operate, with ongoing monitoring, adjustment, and alerting based on real-time conditions.
[0064] Thus the present disclosure provides the system (100) that offers an advanced solution for continuous real-time monitoring of motor (102) and pump (104).
[0065] It will be apparent to those skilled in the art that the apparatus of the disclosure may be provided using some or all of the mentioned features and components without departing from the scope of the present disclosure. While various embodiments of the present disclosure have been illustrated and described herein, it will be clear that the disclosure is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the disclosure, as described in the claims.

ADVANTAGES OF THE PRESENT INVENTION
[0066] The present disclosure provides a system that continuously monitors operational status of each individual component in real-time.
[0067] The present disclosure provides a system capable of continuously tracking the operational status of each component in real-time.
[0068] The present disclosure provides a system that identifies deviations from standard performance and categorizes faults based on their severity for efficient diagnostics.
[0069] The present disclosure provides a system that anticipates deviations before they result in component failure.
[0070] The present disclosure provides a system that delivers real-time notifications upon detecting performance deviations or faults.
[0071] The present disclosure provides a system that enhances energy efficiency by regulating motor and pump performance based on the required flow rate.
[0072] The present disclosure provides a system that facilitates dynamic modifications to operational parameters in response to user inputs received via wired and wireless communication interfaces.
[0073] The present disclosure provides a system that maintains seamless adjustments between varying flow rate levels to avoid sudden operational shifts.
[0074] The present disclosure provides a system that mitigates mechanical wear and tear by reducing performance output when maximum capacity is unnecessary.
[0075] The present disclosure provides a system that supports remote monitoring and enables real-time data collection for analysis.
[0076] The present disclosure provides a system that improves reliability, reduces operational downtime, and prolongs component life through predictive maintenance methodologies.
 
, Claims:1. A motor and pump system (100) comprising:
a motor (102) and a pump (104), each comprising a plurality of components;
a signal transmission unit (106) operatively coupled with each component, configured to generate and transmit signals indicative of operational conditions of the respective component; and
a control unit (108) communicably coupled to the signal transmission unit (106) and a memory (110), wherein the memory (110) stores processor-executable instructions that, when executed by the control unit (108), cause the control unit (108) to:
extract features from the signals received from the signal transmission unit (106);
compare the extracted features with pre-stored threshold values in the memory (110);
detect deviations based on the comparison; and
generate an alert upon detection of at least one deviation, wherein the generated alert being transmitted to a computing device (112).

2. The motor and pump system (100) as claimed in claim 1, wherein the operational conditions of each component are determined based on at least one of: rotational speed, torque, power consumption, temperature, vibration, pressure, or fluid flow rate.

3. The motor and pump system (100) as claimed in claim 1, wherein the features comprise at least one of: amplitude, frequency, phase, waveform pattern, signal distortion, noise levels, or duty cycle variations.

4. The motor and pump system (100) as claimed in claim 1, wherein the control unit (108) applies signal processing techniques to extract the features from the received signals.
5. The motor and pump system (100) as claimed in claim 1, wherein the extracted features are analyzed using pattern recognition techniques to detect early-stage anomalies indicative of failure of the respective components.

6. The motor and pump system (100) as claimed in claim 1, wherein the control unit (108) classifies the detected deviations into predefined fault categories and determines severity of each detected fault based on deviation magnitude and historical failure patterns.

7. The motor and pump system (100) as claimed in claim 1, wherein the detected deviations trigger a corrective response comprising at least one of: adjusting operational parameters, initiating a controlled shutdown, or scheduling maintenance.

8. The motor and pump system (100) as claimed in claim 1, wherein the control unit (108) is further configured to:
dynamically adjusts the pre-stored threshold values based on real-time performance metrics of the respective components, wherein the real-time performance metrics of each component are evaluated based on at least one of: the operational conditions, environmental factors, historical performance data, or the detected deviations; and
applies machine learning techniques to analyze the historical performance data stored in the memory and predict the deviations in at least one of the plurality of components prior to occurrence.

9. The motor and pump system (100) as claimed in claim 1, wherein the control unit (108) is communicably coupled to a server, enables the server to receive real-time data from the control unit.

10. The motor and pump system (100) as claimed in claim 2, wherein the control unit (108) receives inputs through a graphical user interface (GUI) on the computing device (112), and the control unit (108) is configured to:
dynamically adjusts motor speed, pump pressure, and valve positioning in real-time based on the received inputs for flow rate adjustment; and
adjusts the power consumption of the motor (102) and the pump (104) proportionally to set flow rate.