CN119416113A

CN119416113A - A method and system for monitoring energy equipment health based on big data

Info

Publication number: CN119416113A
Application number: CN202411499943.3A
Authority: CN
Inventors: 金小筠; 王智勃
Original assignee: Beijing Ruizhide Information Technology Co ltd
Current assignee: Beijing Ruizhide Information Technology Co ltd
Priority date: 2024-10-25
Filing date: 2024-10-25
Publication date: 2025-02-11

Abstract

The invention discloses a health monitoring method and a system of energy equipment based on big data, which relate to the technical field of health monitoring of energy equipment and comprise the steps of configuring a sensor to collect data, storing the data in a central database through an industrial Internet of things, and processing the data; and carrying out data analysis and identification trend, calculating health index, carrying out fault prediction and implementing maintenance optimization. The health monitoring method of the energy equipment based on big data provided by the invention can accurately reflect the long-term state of the equipment by analyzing and identifying the trend through the data, ensures the accuracy and consistency of the data by adopting a standardized communication protocol, provides reliable data for subsequent analysis, and enables a model to pay more Attention to the time period related to the current fault in the historical data by adopting the LSTM combined with an Attention mechanism and weighting the data of different time steps, thereby greatly improving the accuracy of prediction.

Description

Big data-based energy equipment health monitoring method and system

Technical Field

The invention relates to the technical field of health monitoring of energy equipment, in particular to a health monitoring method and system of energy equipment based on big data.

Background

Along with the rapid development of big data technology and industrial Internet of things (IIoT), the health monitoring of energy equipment is gradually changed to the intelligent direction of data driving. By deploying various sensors to collect equipment operation data and combining a big data analysis technology, the energy equipment can be monitored and maintained in real time. The traditional equipment monitoring mode mainly depends on periodic manual inspection and simple threshold alarming, and the introduction of a big data technology can be used for efficiently processing massive multi-dimensional equipment data, extracting valuable information and providing data support for equipment fault detection, performance evaluation and trend prediction. By using the big data analysis model, abnormal states and potential problems in the operation of the energy equipment can be identified in advance, so that a basis is provided for predictive maintenance and intelligent optimization. The technical progress greatly improves the management efficiency and the operation reliability of the energy equipment, and reduces the unplanned shutdown and the operation loss caused by equipment failure.

Despite the initial application of big data analysis in energy plant monitoring, the prior art still has various limitations. First, many existing systems are limited to basic data collection and simple statistical analysis, lacking in deep mining of complex correlations between data. Because of the complex running environment of the equipment and various data characteristics, the traditional method is difficult to accurately capture long-term trends and potential equipment health hidden dangers. In addition, the prior art mostly relies on static threshold values or rules to perform fault early warning, neglects dynamic changes of the running state of equipment, easily causes the problem of false alarm or missing report, and influences the timeliness and accuracy of equipment maintenance. Secondly, in the aspect of fault prediction, the conventional method generally adopts a linear model or a simple regression algorithm, and nonlinear characteristics in equipment operation data are difficult to deal with, so that the prediction effect is poor.

Disclosure of Invention

The present invention has been made in view of the above-described problems.

Therefore, the invention solves the technical problems that the existing energy equipment health monitoring method has insufficient analysis on the running state of complex equipment, low fault prediction precision, incapability of dynamically adapting to the state change of the equipment and more accurate health state evaluation and fault prediction by big data analysis.

The technical scheme includes that the energy equipment health monitoring method based on big data comprises the steps of configuring sensors to collect data, storing the data in a central database through an industrial Internet of things, processing the data, analyzing and identifying data, calculating health indexes, predicting faults, and implementing maintenance optimization.

The method for monitoring the health of the energy equipment based on the big data is characterized in that the configuration sensor collects data, the data are stored in a central database through an industrial Internet of things, the data are collected through a sensor installed in the energy equipment, the sensor comprises a temperature sensor, a pressure sensor and a vibration sensor, the data are stored in the central database through the industrial Internet of things, the data are processed, the data are collected in real time through a standardized communication protocol Modbus, and the data are transmitted to the central database through a wired or wireless industrial Internet of things IIoT gateway.

The method for monitoring the health of the energy equipment based on the big data comprises the steps of detecting abnormal values by means of a statistical method, detecting abnormal points in the data, marking and removing the abnormal data, estimating the missing values by means of an interpolation method, filling the data missing caused by faults or communication interruption of a sensor, carrying out normalization processing on the data, storing the processed data in a central database again, and carrying out classified management on the data according to equipment types by means of a partition storage strategy.

The invention relates to a method for monitoring health of energy equipment based on big data, which comprises the following steps of carrying out data analysis and identification on trend, calculating trend values of various operation data of the equipment through time sequence analysis, reflecting long-term state of the equipment, and representing as follows:

Wherein T (T) represents a trend value at time T, X (T) is equipment operation data acquired at time T, alpha is a smoothing factor, the degree of trend smoothness is controlled, k is a high-order filter weight for capturing acceleration changes, Indicating acceleration changes in the device data.

The method for monitoring the health of the energy equipment based on big data comprises the following steps of analyzing trend change, analyzing the health index of the energy equipment when the trend change exceeds a preset change speed limit, and inputting trend values into a nonlinear mapping model to be expressed as:

Wherein x _i is a trend value of equipment data obtained from different sensors after trend analysis, gamma _i、β_i、λ_i is a parameter for controlling the amplitude and regularization degree self-adaption of the nonlinear mapping, f _i(x_i) is a Sigmoid function, Z is a normalization factor, alpha _i is nonlinear weight, and the trend value of each sensor is mapped to health index HI in a nonlinear mode by the system under the control of self-adaption parameters gamma _i、β_i and lambda _i;

and if the health index HI is more than or equal to 80, the equipment is in a health state, and fault prediction is not needed.

The invention relates to a method for monitoring the health of energy equipment based on big data, which comprises the following steps that when the equipment is not in a health state, a health system automatically judges that the equipment is a potential fault signal, an LSTM prediction model is started, the probability of future faults is estimated, a health index HI (t) is used as input sequence data, and the input sequence data is transmitted into the fault prediction model to be expressed as:

Wherein h _t represents the hidden state of the time t, HI (t) represents the health index of the device at the time t, W _h and b _h are respectively a weight matrix and a bias, the weight matrix and the bias are set according to different energy devices, s _t,j is a scoring function in an Attention mechanism, k is a sequence length, v _t,j is a weight matrix corresponding to s _t,j, and eta.W _h∥² is not a regularization term.

The implementation and maintenance optimization comprises monitoring all sensor data and health indexes of equipment in real time through an industrial Internet of things platform, performing data visualization, displaying the data to operation and maintenance personnel, setting a multi-stage early warning mechanism, sending warning to the operation and maintenance personnel, and automatically generating an analysis report of equipment fault reasons according to historical data of the equipment.

Another object of the present invention is to provide an energy device health monitoring system based on big data, which can calculate trend values of devices in different time periods through time series analysis technology, capture severe changes in device operation, not only identify smooth changes in device operation, but also make a sensitive reaction to rapid fluctuation, and by identifying potential fault symptoms, enhance early detection capability of device faults, and solve the problem that the current energy device health monitoring method cannot accurately evaluate health states and predict faults.

The health monitoring system for the energy equipment based on the big data comprises a data acquisition module, a health assessment module and a fault prediction module, wherein the data acquisition module is used for configuring a sensor to acquire data, the data are stored in a central database through an industrial Internet of things and are subjected to data processing, the health assessment module is used for carrying out data analysis and identification trend and calculating health index, and the fault prediction module is used for carrying out fault prediction and implementing maintenance optimization.

A computer device comprising a memory storing a computer program and a processor executing the computer program is a step of implementing a big data based energy device health monitoring method.

A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a big data based energy device health monitoring method.

The method has the advantages that the trend is identified through data analysis, the long-term state of the equipment can be accurately reflected, the accuracy and consistency of the data are guaranteed through a standardized communication protocol, reliable data are provided for subsequent analysis, the LSTM combined with an Attention mechanism is adopted, the data of different time steps are processed through weighting, the model can pay more Attention to the time period related to the current fault in the historical data, and accordingly the prediction accuracy is greatly improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is an overall flowchart of an energy device health monitoring method based on big data according to a first embodiment of the present invention.

Fig. 2 is an overall flowchart of an energy device health monitoring system based on big data according to a third embodiment of the present invention.

Detailed Description

So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

Embodiment 1, referring to fig. 1, provides a method for monitoring health of an energy device based on big data, which includes:

S1, configuring a sensor to collect data, storing the data in a central database through the industrial Internet of things, and processing the data.

Further, configuring the sensor to collect data, storing the data in the central database through the industrial internet of things comprises collecting operation data through installing the sensor in the energy equipment, wherein the sensor comprises a temperature sensor, a pressure sensor and a vibration sensor.

The data are stored in a central database through the industrial Internet of things, the data processing comprises the step of collecting equipment operation data in real time by adopting a standardized communication protocol Modbus, and the data are transmitted to the central database through a wired or wireless industrial Internet of things IIoT gateway.

In the process of data acquisition, the sensor needs to set reasonable acquisition frequency, and generally sets high-frequency acquisition for key components such as high-rotation-speed equipment or high-load equipment, and can reduce the acquisition frequency for relatively stable components. The data of all the sensors are synchronized through the time stamps, so that the data in the central database has a unified time reference, and the subsequent analysis is supported.

The data is transmitted to a central database, which may be a local high performance server cluster or cloud database. In order to improve expansibility and fault tolerance, a distributed storage scheme, such as a NoSQL database or a cloud data warehouse Amazon S, is adopted.

In order to ensure the real-time performance and the integrity of the data, a redundant data acquisition and transmission mechanism is adopted. When the main system fails, the backup system automatically takes over, so that the data cannot be lost.

It should be noted that performing data processing includes detecting abnormal values by using a statistical method, detecting abnormal points in data, marking and removing abnormal data.

And estimating the missing value by adopting an interpolation method, and filling the data missing of the sensor caused by the fault or the communication interruption.

And carrying out normalization processing on the data, re-storing the processed data in a central database, and carrying out classification management on the data according to the equipment type by adopting a partition storage strategy.

S2, carrying out data analysis and identification on the trend, and calculating the health index.

Further, performing data analysis to identify trends includes calculating trend values for various operational data of the device through time series analysis, reflecting long term status of the device, expressed as:

Wherein T (T) represents a trend value at time T, X (T) is equipment operation data acquired at time T, alpha is a smoothing factor, the degree of trend smoothness is controlled, k is a high-order filter weight for capturing acceleration changes, Indicating acceleration changes in the device data. The trend analysis can capture smooth changes in the running process of the equipment, can sensitively respond to rapid fluctuation, and can identify potential fault symptoms. The higher derivative term enhances sensitivity to severe changes and can help discover the likelihood of equipment failure in advance.

It should be noted that calculating the health index includes analyzing trend changes, and when the trend changes exceed a preset change speed limit, performing health index analysis on the energy device, and inputting trend values into a nonlinear mapping model, which is expressed as:

Wherein x _i is a trend value of equipment data obtained from different sensors after trend analysis, gamma _i、β_i、λ_i is a parameter for controlling the amplitude and regularization degree self-adaption of the nonlinear mapping, f _i(x_i) is a Sigmoid function, Z is a normalization factor, and alpha _i is nonlinear weight. The system maps trend values of each sensor to the health index HI in a non-linear manner through control of the adaptive parameters gamma _i、β_i and lambda _i.

And S3, performing fault prediction and implementing maintenance optimization.

Further, performing the fault prediction includes automatically determining, by the health system, that the device is not in a healthy state, a potential fault signal of the device, starting an LSTM prediction model, evaluating a probability of a future fault, and inputting the health index HI (t) as input sequence data into the fault prediction model, expressed as:

Wherein h _t represents the hidden state of the time t, HI (t) represents the health index of the device at the time t, W _h and b _h are respectively a weight matrix and a bias, the weight matrix and the bias are set according to different energy devices, s _t,j is a scoring function in an Attention mechanism, k is a sequence length, v _t,j is a weight matrix corresponding to s _t,j, and eta.W _h∥² is not a regularization term. Compared with the traditional LSTM model, the invention adds an Attention mechanism, so that the model can focus on the time step most relevant to the fault in the historical data. The application of the Attention mechanism in time sequence analysis greatly improves the accuracy of fault prediction, and particularly when long sequence data is processed, the system can more effectively mine out key time period information causing equipment faults by automatically distributing Attention weights. This is an innovation point that is difficult to achieve in the prior art, and conventional prediction models generally cannot dynamically adapt to changes in equipment states, and cannot accurately locate potential occurrence times of faults.

In conclusion, the invention realizes accurate and real-time energy equipment fault prediction through dynamic health monitoring and fault prediction based on LSTM and Attention mechanisms. The method not only can improve the operation efficiency of the equipment and reduce unplanned shutdown, but also can cope with complex operation environments of different equipment through the self-adaptive characteristic of the deep learning model, and is remarkably superior to the prior art.

It should be noted that implementing maintenance optimization includes monitoring various sensor data and health indexes of equipment in real time through an industrial internet of things platform, performing data visualization, displaying the data to operation and maintenance personnel, setting a multi-stage early warning mechanism, sending warning to the operation and maintenance personnel, and automatically generating an analysis report of equipment failure reasons according to historical data of the equipment.

In order to facilitate the monitoring of equipment by operation and maintenance personnel, the industrial internet of things platform provides a graphical visual interface to convert real-time data into readable charts, dashboards or other interactive visual tools. The specific design comprises the following steps:

And the trend analysis chart shows the historic and real-time trend of various sensor data through the time sequence chart, so that an operation and maintenance person can easily identify the change mode in the running process of the equipment. This includes historical plots of key data for equipment temperature, vibration frequency, pressure fluctuations, etc., to grasp the long-term operating conditions of the equipment.

Health index instrument panel the health index can be displayed in the form of instrument panel, and the health status can be divided into different color intervals, for example green indicates health status, yellow indicates maintenance is needed, and red indicates that fault state is predicted. The visual display mode can help operation and maintenance personnel to quickly know the current operation condition of the equipment.

And marking abnormal data, namely marking abnormal points by the system in a chart mode when abnormal fluctuation occurs to the data, and reminding operation and maintenance personnel to perform key monitoring or further analysis. The visual anomaly labeling can better help operation staff to find potential problems, and reduce complexity of data analysis.

After the equipment triggers the early warning, the system not only gives an alarm, but also automatically generates a fault cause analysis report of the equipment. The report is based on historical data for deep analysis, and fault reasons of equipment are automatically mined through big data analysis and machine learning models such as decision trees. The specific process comprises the following steps:

The system firstly extracts historical operation data of the equipment, compares the historical operation data with the current abnormal data and analyzes whether a similar operation mode exists. Identify whether similar problems have been experienced before the discovery device and provide corresponding processing experience.

The system is able to identify possible failure modes of the device. For example, whether or not the abnormal rise in temperature is associated with a severe fluctuation in vibration frequency, thereby judging whether or not there is a problem of wear of mechanical parts or overload of the motor, or the like.

While generating the fault analysis report, the system automatically generates maintenance suggestions according to the historical data and the current equipment state. This includes when a component change is made, whether an immediate shutdown is required, and future maintenance cycle adjustments. The function can help operation and maintenance personnel to make a more targeted maintenance plan, and avoid sudden faults of equipment.

Embodiment 2 an embodiment of the invention provides a health monitoring method of energy equipment based on big data, and in order to verify the beneficial effects of the invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

Firstly, in order to verify the effectiveness and innovation of the health monitoring method of the energy equipment based on big data, the test is carried out on an industrial wind driven generator. The running state of the generator is monitored in real time through an industrial Internet of things (IIoT) platform, and the data acquisition comprises key parameters such as temperature, pressure and vibration of equipment. The acquisition frequency of each sensor is set according to the importance of the components, all data are transmitted to a central database through a IIoT gateway by adopting a Modbus communication protocol, and the real-time performance and accuracy of the data are ensured. The specific experimental data are shown in table 1.

Table 1 comparison table of experimental data

First, the health index decreases with increasing temperature, pressure and vibration frequency, and it can be seen from the table that the health index gradually decreases from 85 to 65, indicating a gradual deterioration of the operating state of the apparatus. Compared with the prior art relying on the examination of a fixed time period, the invention can dynamically adjust the health state through real-time data analysis and timely reflect the health change of equipment.

The fault prediction probability in the table is also gradually increased, which shows how the introduction of the LSTM and the Attention mechanism improves the prediction accuracy. Along with the deterioration of the operation parameters of the equipment, the system can dynamically identify key historical time steps related to faults (such as the sudden acceleration rise of vibration frequencies of 10:45 and 11:00) and send emergency early warning when the health index is reduced to 70 so as to predict the possible future fault probability, and the traditional fault prediction model cannot flexibly cope with the nonlinear changes.

Furthermore, maintenance recommendations are gradually evolving from "normal" to "maintenance needed" and "emergency checks". By combining with the failure prediction module, the system can not only detect potential problems of equipment, but also provide specific maintenance suggestions for operation and maintenance personnel. The maintenance optimization strategy based on big data analysis can greatly reduce the probability of sudden faults of equipment, reduce unplanned downtime and remarkably improve the operation efficiency of the equipment.

Embodiment 3, referring to fig. 2, for an embodiment of the present invention, a health monitoring system for energy equipment based on big data is provided, which includes a data acquisition module, a health evaluation module, and a fault prediction module.

The data acquisition module is used for configuring the sensor to acquire data, and the data are stored in the central database through the industrial Internet of things and are processed. The health evaluation module is used for carrying out data analysis and identification trend and calculating health index. The fault prediction module is used for performing fault prediction and implementing maintenance optimization.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. The storage medium includes a U disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, an optical disk, or other various media capable of storing program codes.

Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

More specific examples (a non-exhaustive list) of the computer-readable medium include an electrical connection (an electronic device) having one or more wires, a portable computer diskette (a magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of techniques known in the art, discrete logic circuits with logic gates for implementing logic functions on data signals, application specific integrated circuits with appropriate combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like. It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.

It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.

Claims

1. A method for monitoring the health of energy equipment based on big data, comprising:

Configure sensors to collect data, store it in a central database through the Industrial Internet of Things, and process the data;

Conduct data analysis to identify trends and calculate health indices;

Predict failures and optimize maintenance.

2. The method for monitoring the health of energy equipment based on big data as claimed in claim 1, characterized in that: the configuring sensors to collect data and storing it in a central database through the industrial Internet of Things includes collecting operating data by installing sensors in the energy equipment, the sensors including temperature sensors, pressure sensors, and vibration sensors;

The storage in a central database through the Industrial Internet of Things and the data processing include using the standardized communication protocol Modbus to collect equipment operation data in real time, and transmitting the data to the central database through a wired or wireless Industrial Internet of Things IIoT gateway.

3. The method for monitoring the health of energy equipment based on big data according to claim 2, characterized in that: the data processing includes using statistical methods to detect abnormal values, detect abnormal points in the data, mark and remove abnormal data;

Interpolation is used to estimate missing values and fill in missing data caused by sensor failure or communication interruption;

The data is normalized and stored back in the central database. A partition storage strategy is adopted to classify and manage the data according to the device type.

4. The method for monitoring the health of energy equipment based on big data according to claim 3, characterized in that: the data analysis and trend identification comprises calculating the trend value of each operating data of the equipment through time series analysis, reflecting the long-term status of the equipment, which is expressed as:

Where T(t) represents the trend value at time t, X(t) is the equipment operation data collected at time t, α is the smoothing factor, which controls the degree of trend smoothing, and k is the high-order filter weight, which is used to capture acceleration changes. Indicates the acceleration change of device data.

5. The method for monitoring the health of energy equipment based on big data according to claim 4, characterized in that: the calculation of the health index includes analyzing the trend change, when the trend change exceeds the preset change speed limit, the health index analysis is performed on the energy equipment, and the trend value is input into the nonlinear mapping model, which is expressed as:

Wherein, _xi is the trend value of the equipment data obtained from different sensors after trend analysis, _γi , _βi , _λi are the parameters for controlling the amplitude of nonlinear mapping and the degree of regularization, _fi ( _xi ) is the Sigmoid function, Z is the normalization factor, and _αi is the nonlinear weight; through the control of the adaptive parameters _γi , _βi and _λi , the system maps the trend value of each sensor to the health index HI in a nonlinear manner;

If the health index HI ≥ 80, the device is considered to be in a healthy state and no fault prediction is required.

6. The energy equipment health monitoring method based on big data as claimed in claim 5 is characterized in that: the fault prediction includes when the equipment is not in a healthy state, the health system automatically determines it as a potential fault signal of the equipment, starts the LSTM prediction model, evaluates the probability of future failures, and uses the health index HI(t) as input sequence data to pass into the fault prediction model, which is expressed as:

Among them, h _t represents the hidden state at time t, HI(t) represents the health index of the device at time t, W _h and b _h are the weight matrix and bias, respectively, which are set according to different energy devices, s _t,j is the scoring function in the Attention mechanism, k is the sequence length, v _t,j is the weight matrix corresponding to s _t,j , and η ∥ W _h ∥ ² is the regularization term.

7. The energy equipment health monitoring method based on big data as described in claim 6 is characterized in that: the implementation of maintenance optimization includes real-time monitoring of various sensor data and health indexes of the equipment through the industrial Internet of Things platform, and data visualization, displaying the data to operation and maintenance personnel, setting up a multi-level early warning mechanism, issuing warnings to operation and maintenance personnel, and automatically generating an analysis report on the cause of equipment failure based on the historical data of the equipment.

8. A system using the big data-based energy equipment health monitoring method as claimed in any one of claims 1 to 7, characterized in that it comprises a data acquisition module, a health assessment module, and a fault prediction module;

The data acquisition module is used to configure sensors to collect data, store it in a central database through the industrial Internet of Things, and perform data processing;

The health assessment module is used to perform data analysis to identify trends and calculate health indexes;

The fault prediction module is used to perform fault prediction and implement maintenance optimization.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the energy equipment health monitoring method based on big data as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the energy equipment health monitoring method based on big data described in any one of claims 1 to 7 are implemented.