AN IoT BASED SYSTEM FOR ANTICIPATING BEARING FAILURES IN MOTORS

Abstract

ABSTRACT AN IoT BASED SYSTEM FOR ANTICIPATING BEARING FAILURES IN MOTORS The present invention relates to an IoT-based system for anticipating and preventing bearing failures in motors, particularly small to medium-sized motors. The system includes one or more sensors configured to continuously collect real-time operational data, such as vibration frequency, temperature, current, and total harmonic distortion (THD), antenna. A communication module wirelessly transmits this data to a cloud server and processor. The processor, utilizing machine learning algorithms, analyzes the data to predict potential bearing failures based on identified patterns. A real-time intelligent module further builds and trains the system to improve failure predictions. The system capable to send maintenance alerts to electronic devices allowing timely intervention. The invention is specifically designed for motor analysis and can be customized for various motor types.

Specification

Description:FIELD OF INVENTION
The present invention relates to predictive maintence using IoT. The present invention specifically relates to the field of Industrial IoT (IIoT), predictive maintenance, and machine learning. It aims to enhance the reliability and efficiency of induction motors used in various industrial applications by enabling real-time monitoring via predictive analytics.

BACKGROUND OF THE INVENTION
The critical role that induction motors and their bearings play in various industries, and how important it is to maintain them properly to avoid costly failures. Traditional maintenance strategies have their limitations, as you've outlined, which often result in downtime, inefficiency, and unoptimized performance.
The Limitations of Traditional Approaches like Reactive Maintenance typically involves fixing or replacing motor parts only after a failure occurs. While it may seem cost-effective in the short term, it is highly inefficient. Unexpected failures can lead to unplanned downtimes that disrupt production, leading to high repair costs due to the need for emergency services, Increased risk of collateral damage to other components, lost production time, which can have severe financial consequences in industries where continuous operation is critical.
Traditional Preventive Maintenance: This strategy involves performing routine checks or part replacements based on fixed intervals (e.g., every few months or after a certain number of operating hours). While it can help avoid sudden breakdowns, it has its own set of drawbacks. It doesn't always account for the actual condition of the motor, which means parts may be replaced prematurely, leading to wasted resources and unnecessary downtime. Scheduled checks might miss early signs of wear, resulting in failures occurring between maintenance intervals. Sometimes, by replacing parts based solely on time, motors may still experience performance degradation or failure due to undetected underlying issues.
Several research has been going on in the said technology and several prior arts discuss the same as the patent application CN209858161 U titled as "Explosion-Proof Motor Fault Monitoring System Based On NB-IoT" discloses an explosion-proof motor fault monitoring system based on NB-IoT. The system comprises a fault detection module, a control module, a cloud server and a mobile terminal, the fault detection module comprises a vibration detection device, an eccentricity detection device, a combustible gas detection device and a power failure detection device; the control module comprises a single-chip microcomputer and an NB-IoT module. According to the present invention various motor faults can be detected in real time and the system can be widely applied to the field of motor fault monitoring.
Another patent application KR20190117844 A titled as "IoT Monitoring System for Detecting Error Of Motor Valve" discusses an IoT network-based motor valve failure detection and failure prediction monitoring system which detects or predicts a failure of a motor valve widely used in an industrial site, and informs a terminal of a person in charge of the terminal to enable appropriate and rapid response. According to the present invention, the IoT network-based motor valve failure detection and failure prediction monitoring system comprises: a defect detection device connected to a control line of a motor valve to detect or predict a failure of the motor valve; a management server for managing motor valves by communicating with a plurality of defect detection devices based on a network; and a manager terminal for receiving and monitoring information on the motor valve from the management server.
Yet another invention CN109633444 A titled as "IoT (Internet of Things) Based Distributed Motor Fault Monitoring System and Method" discusses an IoT based distributed motor fault monitoring system and method. The monitoring system is formed by taking a server as a single central node and power distribution cabinets as tail-end nodes via network and wireless digital communication technologies. The system comprises motor onsite parameter monitoring devices arranged in different motors, a monitoring center and a handheld terminal for a monitoring staff. The motor onsite parameter monitoring device uploads monitored motor operation data to the power distribution cabinet wirelessly, the server gathers the monitoring parameters of the different nodes, the monitoring parameters are displayed in a terminal display screen, and when a parameter exceeds the standard and reaches a fault value, alarm is raised, and alarm information displayed in the terminal display screen and sent to the handheld terminal of the monitoring staff. Thus, the monitoring staff in the monitoring center can comprehensively observe the operation parameters of all motors, and receive alarm information against abnormity timely, faults can be eliminated timely, and production or safety accidents caused by the motor fault can be avoided.
Though there are several prior arts which discuss IoT based monitoring none of them address the of both reactive and preventive maintenance strategies. None of them discuss a Condition-Based Monitoring (CBM) which is more efficient and cost-effective approach. CBM leverages real-time data from sensors and monitoring systems to assess the actual health and condition of the motor, including its bearings, and make maintenance decisions based on this data.
The present invention addresses the said issues and focusses on the condition-based monitoring, when paired with predictive analytics, takes motor health management a step further. By collecting data over time and using machine learning or statistical models, predictive maintenance can forecast when a motor is likely to fail based on patterns and trends in the data. This allows for even more precise scheduling of repairs and replacements.
OBJECTS OF THE INVENTION
The primary objective of the present invention is to provide an IoT based system for anticipating bearing failures in motors which is assisted with AI and machine learning.
It is another objective of the present invention to provide an an IoT based system for anticipating bearing failures in motors primarily comprising sensors, cloud connectivity and Machine learning and data analytics as their key components.
DRAWINGS
Fig 1 Illustrates the block diagram of IoT based system for anticipating bearing failures in motors
Fig 2 Illustrates the Block Diagram of Real Time Intelligent Module

SUMMARY OF THE INVENTION
The following summary is provided to facilitate a clear understanding of the new features in the disclosed embodiment and it is not intended to be a full, detailed description. A detailed description of all the aspects of the disclosed invention can be understood by reviewing the full specification, the drawing and the claims and the abstract, as a whole.
The invention provides an IoT-based system for predicting and preventing bearing failures, primarily in small to medium-sized motors. The system comprises of
• A plurality of sensors sensors, collect real-time operational data, including vibration frequency, temperature, current, and total harmonic distortion (THD), antenna which are transmitted wirelessly via the communication module to a cloud server and processing unit.
• a communication module, coupled to the IoT sensor, configured to wirelessly transmit the real-time sensor data to a cloud server for storage and to processor for processing;
• a processor, equipped with machine learning algorithms, analyzes the data to detect early patterns indicative of bearing failures
• a real-time intelligent module, which is built upon both training and testing data, improves the accuracy of failure predictions over time. By continuously analysing sensor data, the system provides real-time monitoring and early warnings of impending bearing failures, allowing for preventive maintenance.
Additionally, the system is designed to send alerts to various electronic devices, including mobile phones, tablets, and laptops, notifying the user when maintenance is required. This feature enhances operational efficiency and reduces downtime by ensuring timely interventions. The system is particularly suited for the analysis of motors but can be adapted for broader applications. The technology offers a scalable solution that is easily customizable for different motor sizes and operational environments.
DETAILED DESCRIPTION OF THE INVENTION
Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the disclosure. Thus, the following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of the disclosure. However, in certain instances, known details are not described in order to avoid obscuring the description.
References to one or an embodiment in the present disclosure can be references to the same embodiment or any embodiment; and such references mean at least one of the embodiments.
Reference to "one embodiment", "an embodiment", "one aspect", "some aspects", "an aspect" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others.
The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Alternative language and synonyms may be used for any one or more of the terms discussed herein, and no special significance should be placed upon whether or not a term is elaborated or discussed herein. In some cases, synonyms for certain terms are provided.
Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or can be learned by practice of the herein disclosed principles. The features and advantages of the disclosure can be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the disclosure will become more fully apparent from the following description and appended claims or can be learned by the practice of the principles set forth herein.
This invention falls relates to the Industrial IoT (IIoT), predictive maintenance, and machine learning. It aims to enhance the reliability and efficiency of induction motors used in various industrial applications by enabling real-time monitoring and predictive analytics. The invention is a predictive maintenance system that uses Internet of Things (IoT) technology and hybrid machine learning models to monitor and analyze the condition of bearings in induction motors. By continuously collecting real-time data from sensors (like vibration frequency, temperature, current, and THD, antenna) the module detects early signs of bearing wear or failure. This system can prevent unexpected motor breakdowns, reduce downtime, and optimize maintenance schedules by predicting failures before they occur, thus improving the overall operational efficiency of industrial machinery.
With the advent of IoT and advancements in sensor technology, it became possible to implement predictive maintenance, which leverages real-time monitoring to anticipate and prevent equipment failures. The present invention relies on an approach of Condition-Based Monitoring (CBM) and Predictive Maintenance heavily relies on three key technological components: IoT sensors, cloud connectivity, and machine learning. These technologies work together to enable real-time, data-driven decision-making and to forecast potential motor failures before they occur. Let's break down each of these components and how they contribute to a robust predictive maintenance system.
1. IoT Sensors
Internet of Things (IoT) sensors are the foundation of modern predictive maintenance systems. These small, affordable devices are strategically placed on critical components of the induction motor, such as the bearings, motor windings, shafts, and other moving parts. The sensors continuously collect real-time data on key parameters that indicate the health and performance of the motor.
The sensors include vibration sensors, acoustic sensors, temperature sensors, current, antenna and voltage sensors which sense the vibration, noise, temperatures, current and voltage respectively in real-time.
2. Cloud Connectivity
Once the data is collected by IoT sensors, it needs to be transmitted to a centralized system for further analysis. This is where cloud connectivity comes into play. The sensor information is collected and transmitted to cloud via wireless connectivity. Cloud systems enable real-time data access for operators and maintenance teams. If a sensor detects a potential problem, an alert is generated immediately, allowing operators to take proactive steps without delay.
3. Machine learning and data analytics
The heart of predictive maintenance lies in machine learning (ML) and advanced analytics. Machine learning algorithms analyze the historical and real-time data gathered from the IoT sensors to detect patterns and predict failures before they happen.
Together, these three components form a smart ecosystem that enhances the efficiency, safety, and reliability of induction motors. Here's how they integrate:
1. Real-Time Monitoring: IoT sensors provide a continuous stream of data that is sent to the cloud for centralized storage and real-time analysis.
2. Data Aggregation and Analysis: The cloud platform aggregates and organizes this data from multiple sources and provides access to real-time insights and reports.
3. Predictive Insights: Machine learning algorithms analyse this data to provide predictive insights, enabling operators to anticipate failures and schedule maintenance activities accordingly.
4. Proactive Decision-Making: Maintenance teams can make data-driven decisions, triggering maintenance actions only, when necessary (based on condition rather than scheduled intervals), leading to optimized performance and cost savings.

Components And Working of The Invention
The invention provides an IoT-based system for predicting and preventing bearing failures, primarily in small to medium-sized motors. The system comprises of
• A plurality of sensors, collect real-time operational data, including vibration frequency, temperature, current, and total harmonic distortion (THD), antenna which are transmitted wirelessly via the communication module to a cloud server and processing unit.
• a communication module, coupled to the IoT sensor, configured to wirelessly transmit the real-time sensor data to a cloud server for storage and to processor for processing;
• a processor, equipped with machine learning algorithms, analyzes the data to detect early patterns indicative of bearing failures
• a real-time intelligent module, which is built upon both training and testing data, improves the accuracy of failure predictions over time. By continuously analysing sensor data, the system provides real-time monitoring and early warnings of impending bearing failures, allowing for preventive maintenance. The figure 2 shows the basic structure or block diagram of the real-time intelligent module discussed.
Additionally, the system is designed to send alerts to various electronic devices, including mobile phones, tablets, and laptops, notifying the user when maintenance is required. This feature enhances operational efficiency and reduces downtime by ensuring timely interventions. The system is particularly suited for the analysis of motors but can be adapted for broader applications. The technology offers a scalable solution that is easily customizable for different motor sizes and operational environments. The overview the explained system is provided in the form of block diagram in figure 1.

The key highlights of the present system include:
1) Smart Predictive Analytics: The module likely employs current sensor, voltage sensor, acoustic sensor, THD analyzer, speed sensor, SMA type antenna and hybrid deep learning algorithms, thus improving the classification and prediction accuracy to 99% and also improving the performance metrics like precision, recall and F1 score. The above methodology analyzes data patterns and predict bearing failures, which goes beyond traditional methods of monitoring.
2) Real-Time remote Monitoring: Unlike conventional systems that might rely on periodic checks, this module continuously monitors key performance indicators remotely, providing immediate feedback and alerts for maintenance needs.
3) Analysis of small and medium range motors: The module can analyse small to medium range(0.25 HP to 50 HP) motors as the current sensors used have maximum rated current carrying capacity of 100 A.
4) Cost-Effectiveness: By predicting failures before they lead to significant damage or downtime, the module could save costs associated with emergency repairs and unplanned outages.



, Claims:We Claim,
1. An IoT based system for anticipating and preventing bearing failures comprising:
a. at least one sensor, to configured to continuously collect real-time data on one or more operational parameters of the equipment
b. a communication module coupled to the IoT sensor, configured to wirelessly transmit the real-time sensor data to a cloud server for storage and to processor for processing;
c. a processor or processing unit assisted with machine learning algorithms, configured to receive the real-time sensor data from the communication module and analyse the data using a machine learning algorithm
d. a real-time intelligent module to predict failure, in communication with the processor configured to predict possible bearing failures based on patterns and further building and training of the system, wherein the said system operates in real time.
2. An IoT based system for anticipating and preventing bearing failures as claimed in claim 1 wherein the operational parameters include vibration frequency, temperature, current, and THD.
3. An IoT based system for anticipating and preventing bearing failures as claimed in claim 1 wherein the sensors include current sensor, voltage sensor, acoustic sensor, THD analyser, speed sensor and antenna.
4. An IoT based system for anticipating and preventing bearing failures as claimed in claim 1 wherein the real-time intelligent module is a combination of training data and testing data.
5. An IoT based system for anticipating and preventing bearing failures as claimed in claim 1 wherein the said system can be modified to send alerts for maintenance via any electronic device.
6. An IoT based system for anticipating and preventing bearing failures to send alerts via an electronic device as claimed in claim 5 wherein the electronic device is mobile phone, pad, laptop etc.
7. An IoT based system for anticipating and preventing bearing failures as claimed in claim 1 wherein the said system is built to analysis motors specifically.
8. An IoT based system for anticipating and preventing bearing failures of motors as claimed in claim 7 wherein the motors are small to medium size induction motors.

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