CN112052979A - Equipment spare part demand prediction system based on fault prediction and health management - Google Patents
Equipment spare part demand prediction system based on fault prediction and health management Download PDFInfo
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Abstract
The application relates to a system for predicting the requirement of equipment spare parts based on fault prediction and health management, which comprises a data acquisition and edge calculation module, a data acquisition and edge calculation module and a data analysis and calculation module, wherein the data acquisition and edge calculation module is used for acquiring data by utilizing edge equipment and extracting characteristics required by modeling by analyzing and calculating the data; the data service module is used for uniformly managing data assets of the field equipment and the system and providing data support for the intelligent analysis model; the intelligent analysis module is used for carrying out signal processing and characteristic processing on the acquired data and carrying out health assessment according to the processed signal data; and the visual application module is used for visually displaying the results of the fault prediction and the health assessment. The method and the device are easy to implement, can predict the demand of the spare parts more accurately on the premise of ensuring normal operation of the equipment, minimize the inventory of the spare parts, reduce the maintenance frequency of the equipment, reduce the total maintenance cost of the equipment from the spare parts and the labor cost, and reduce the operation cost of enterprises.
Description
Technical Field
The application relates to a system for predicting equipment spare part requirements based on fault prediction and health management, which is applicable to the technical field of equipment fault and health management.
Background
In the using process of the equipment, the equipment spare parts are necessary guarantee for ensuring the normal operation of the equipment. But the early stocking of spare parts can lead to long-term occupation of enterprise funds; spare parts are not in place timely due to the fact that the spare parts are too long in delivery period, and enterprise equipment cannot be started timely to cause production loss.
The equipment is the basis of guaranteeing production efficiency and product quality of enterprises, and the damage of equipment key parts will directly influence the performance of equipment, leads to equipment trouble to shut down even, influences going on smoothly of production plan, and then leads to the benefit of enterprise impaired. The key parts of the equipment are often expensive and the purchase period is long, and high spare part inventory can occupy enterprise funds and weaken the value created by cash flow of enterprises; if the spare parts are not stored enough, the spare parts are not supplied timely, so that the operation efficiency of the equipment is influenced, and huge economic loss is caused. At present, more algorithms are researched aiming at the prediction of equipment spare parts.
Chinese patent application 201711278040.2 discloses a method and system for predicting the life of spare parts of equipment in a cement plant, the method comprising: acquiring historical operating state data and historical management data of key equipment in a cement plant; labeling the historical operating state data and the historical management data of the key equipment to obtain network training data of each spare part; training a neural network by using the network training data until the training is successful; acquiring current operation state data of target equipment, constructing a state input parameter according to the current operation state data, inputting the state input parameter into a successfully trained neural network, and taking an output result of the neural network as a prediction result of the service life of the spare part.
Chinese patent application 201710354221.2 discloses a method for configuring spare parts of communication equipment based on risk quantification control, which comprises: setting an upper limit value and a lower limit value of the probability that the using quantity of equipment with the same model exceeds the inventory quantity of spare parts in a risk evaluation period which can be borne by the existing network according to empirical values; calculating an upper limit value and a lower limit value of the spare part inventory quantity of equipment with the same model; and configuring the actual inventory quantity of the spare parts of the equipment with the same model according to the calculated upper limit value and lower limit value of the inventory quantity of the spare parts of the equipment with the same model.
Chinese patent application 201410605089.4 discloses a reliability model-based electronic device spare part configuration calculation method, including: determining the minimum acceptable value of the optimized spare part guarantee probability according to the condition of equipment guarantee resources and the requirement of an equipment user, and determining basic configuration parameters of spare part configuration calculation according to the working stress condition and the physical structure of equipment; analyzing the mutual relation among all the parts when the equipment completes the task, analyzing the reliability logic relation among the parts, establishing a reliability model, and calculating the total failure rate of the parts according to a mathematical model corresponding to the reliability model; and constructing an iterative model of spare part configuration calculation to obtain the number of spare parts to be configured of the part.
The above algorithms are more focused on judging and predicting the spare parts based on the replacement data of the spare parts, and the replacement of the spare parts is actually caused by the health status of the equipment and the corresponding parts, so that the algorithms have great limitation and inaccuracy in predicting the spare parts.
On the other hand, prediction of equipment spares generally requires an assessment of the Remaining Useful Life (RUL) of the equipment, with the objective of mining a trend of continued degradation of the equipment from historical monitoring data of the equipment to predict the remaining useful life of the equipment. The remaining useful life of a device is defined as the time that elapses from the current state of the device until the device fails. In most scenarios such as remaining life prediction and health assessment, the health status of the equipment slowly degrades, and therefore the selection of the trend characteristics becomes crucial. In general, features required for the RUL predictive modeling and device health assessment modeling processes need to exhibit four characteristics of degradation, stability (also known as robustness), monotonicity, and consistency throughout the device's full lifecycle. If the above feature selection and evaluation method is adopted singly, the interference of other factors, such as noise, feature mutation and the like, is more or less caused, and the trend feature which really meets the requirement is difficult to select.
Disclosure of Invention
The invention aims to provide an equipment spare part demand prediction system based on fault prediction and health management, which can accurately predict the requirement of a spare part by performing health assessment on equipment and residual life prediction of key components and combining a spare part purchase cycle and an equipment maintenance plan.
The equipment spare part demand prediction system based on fault prediction and health management comprises the following components: the data acquisition and edge calculation module acquires data by using edge equipment and extracts features required by modeling by analyzing and calculating the data; the data service module is used for uniformly managing data assets of the field equipment and the system and providing data support for the intelligent analysis model; the intelligent analysis module is used for carrying out signal processing and characteristic processing on the acquired data and carrying out health assessment according to the processed signal data; and the visual application module is used for visually displaying the results of the fault prediction and the health assessment.
The data acquisition and edge calculation module can align acquired data through a timestamp, then process acquired signal data, and calculate and extract features required by modeling through intelligent modeling analysis in edge equipment. The data service module can be provided with a multi-source data interface and can access data from the data acquisition and edge calculation module and data of an existing system. And the intelligent analysis module multiplies the calculated equipment health index and key component health index values by the risk coefficients of the equipment and the key components respectively, and performs weighting calculation to obtain the replacement priority of the equipment spare parts. The intelligent analysis module is combined with the equipment maintenance plan, the equipment key component fault probability and the residual service life to establish a spare part demand prediction model and predict the minimum spare part inventory.
The intelligent analysis module further comprises a data preprocessing module, a feature extraction module, a feature validity index calculation module and a feature selection module. The characteristic effectiveness index calculation module calculates the degeneration index, the monotonicity index, the robustness index and the consistency index of the characteristic respectively through the degeneration index calculation module, the monotonicity index calculation module, the robustness index calculation module and the consistency index calculation module, and obtains the comprehensive weight index through weighting calculation. The feature selection module reserves the features of which the calculated comprehensive weight index is greater than or equal to a preset weight threshold, omits the features of which the comprehensive weight index is less than the weight threshold, and takes the residual features as the selected tendency indexes.
The data preprocessing module is used for filtering and carrying out analog-to-digital conversion on the acquired data; the feature extraction module obtains a feature vector representing the signal by a feature extraction method; the characteristic effectiveness index calculation module calculates a degeneration index, a monotonicity index, a robustness index and a consistency index of the characteristic respectively through a degeneration index calculation module, a monotonicity index calculation module, a robustness index calculation module and a consistency index calculation module, and obtains a comprehensive weight index through weighting calculation; the feature selection module reserves the features of which the calculated comprehensive weight index is greater than or equal to a preset weight threshold, omits the features of which the comprehensive weight index is less than the weight threshold, and takes the residual features as the selected trend index.
The method for calculating the feature validity index comprises the following steps:
(1) acquiring operation data of equipment through a sensor, and preprocessing the acquired operation data to obtain full life cycle characteristic data of at least one group of equipment;
(2) utilizing a feature extraction method to obtain a plurality of feature vectors representing data signals;
(3) performing degradation index calculation, monotonicity index calculation, robustness index calculation and consistency index calculation on the plurality of feature vectors to respectively obtain a degradation index, a monotonicity index, a robustness index and a consistency index of each feature vector;
(4) setting a weight coefficient for the weight of each index, and obtaining a comprehensive weight index through weighting calculation;
(5) and comparing the comprehensive weight index obtained by calculation with a set weight threshold, and removing the characteristic that the comprehensive weight index is smaller than the weight threshold to be used as an effectiveness index.
According to the method and the system, the fault prediction and health management technology is utilized, the health assessment and the service life prediction are carried out on the equipment and the corresponding parts, the equipment parts, particularly the key parts of the equipment, are subjected to accurate prediction of spare part requirements, and the inventory management of the spare parts is optimized. The system is easy to implement, can predict the demand of the spare parts more accurately on the premise of ensuring the normal operation of the equipment, minimizes the inventory of the spare parts, reduces the maintenance frequency of the equipment, reduces the total maintenance cost of the equipment from the cost of the spare parts and manpower, and finally achieves the aim of reducing the operation cost of enterprises.
Drawings
Fig. 1 shows a structural diagram of the equipment spare part demand prediction system based on fault prediction and health management according to the present application.
Fig. 2 shows a flow chart of the spare part demand forecasting system for forecasting the spare part demand.
Detailed Description
To make the objects, technical solutions and advantages of the present application more apparent, embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
According to the equipment spare part demand prediction system based on fault prediction and health management, as shown in fig. 1, the system is composed of four functional modules of data acquisition and edge calculation, data service, intelligent analysis and visual application.
Data acquisition and edge calculation module
The module utilizes the edge device to collect data and can complete edge calculation in the edge device, thereby reducing the calculation amount and the workload of the server side. Where the collected data includes low frequency data and high frequency data as described below, the data may be aligned by time stamping. The acquired signal data is then processed, and features required for modeling are calculated and extracted in the edge device through intelligent modeling analysis. Meanwhile, the data acquisition and edge calculation module supports unified management of edge intelligent hardware, configuration of data acquisition rules and unified storage and management of data service modules for uploading acquired data to the system. The collected data mainly comprises equipment operation data and physical quantity data of the equipment.
The equipment operation data comprises working time, processing batch information, equipment state, operation process data, alarm information and the like, can be obtained by analyzing an interface protocol opened by the edge intelligent hardware aiming at the equipment controller, the reading frequency of the information is related to the performance of the equipment, and low-frequency data acquisition for several times in one second or once in several seconds can be generally adopted. The collected device operational data may include historical data and real-time data.
The physical quantity data of the equipment, including physical quantity data such as vibration, temperature, sound, rotating speed and the like, can acquire physical quantity signals related to the equipment through a sensor carried by the equipment or externally connected with the equipment. Corresponding filtering and analog-to-digital conversion are carried out through edge intelligent hardware, data acquisition frequency can be selected and configured according to actual requirements, generally, high-frequency data acquisition of thousands of or tens of thousands of seconds is carried out, and a hybrid model based on mechanism and data drive fusion is established by combining equipment operation data.
Data service module
The data service module can realize the unified management of the data assets of the field devices and the system and provide data support for the intelligent analysis model. The module can realize multi-source data access, including data from the data acquisition and edge calculation module and data of an existing system, such as data of an enterprise software system MES, ERP and other systems; unified and standardized storage, calculation and management of data are supported, and classified management and maintenance of low-cost data are realized through standardized and systematized rule management; and the data required by the intelligent analysis module or the visual application module is released in various data release forms such as files and API (application program interface) interfaces.
Intelligent analysis module
In order to realize accurate prediction of spare part requirements of key components of equipment, accurate fault prediction and health management of the equipment are required to be realized, and the health state of the equipment is evaluated. The method comprises the steps of firstly completing data acquisition of field equipment through a data acquisition unit, then carrying out signal processing and characteristic processing on the acquired data, and carrying out health assessment according to the processed signal data. When the equipment health is found to be in a problem, fault diagnosis is carried out, a component in fault and the time when the component is about to be in fault are located, namely the service life prediction of the component is carried out, and related personnel are informed through the visualization of the analysis result.
And multiplying the equipment health index and the key component health index value obtained by analyzing and calculating based on the fault prediction and health management model respectively with the risk coefficients of the equipment and the key component, and performing weighted calculation to obtain the replacement priority of the equipment spare part, wherein the risk coefficients can be obtained by adopting a failure mode and impact analysis (FMEA) method and calculating based on the fault occurrence frequency and the fault generated impact. Then, by combining the equipment maintenance plan, the equipment key component failure probability and the remaining service life, and taking the equipment spare part procurement period and the maintenance personnel work plan as constraint conditions, a spare part demand prediction model is established, and the prediction result is the minimum spare part inventory meeting the minimum maintenance times and the minimum maintenance cost, and the specific flow is shown in fig. 2. It should be noted that the equipment health index in the present application refers to a health index of the whole equipment, and the key component health index is some key components in the equipment. For example, for a complete machine tool, a main shaft is one of the key parts; for the complete wind power generator, a wind wheel is one of the key parts.
In the face of different equipment types, different key components and different faults on the site, different models need to be established, and fault prediction and health management of the key components are realized. Therefore, the intelligent analysis module needs to provide data required by the model from the data layer to the corresponding model, and also needs to perform unified scheduling and execution management on various models running in the system, so that the models are executed at a specified time node, such as a certain time every day, a certain event trigger, and the like. Meanwhile, the operation time, the operation state, the operation result and the like of the model are managed in a unified mode, and the result data of model operation are pushed to the visual interface of the application layer, so that closed-loop management of spare part management of key components of the equipment is formed.
Furthermore, the step of processing the characteristics of the collected data by the intelligent analysis module includes selecting a trend characteristic, and performing comprehensive weight evaluation on the degradation, monotonicity, robustness and consistency indexes of the trend characteristic to predict the remaining service life of the equipment. Specifically, the data preprocessing module may be used to preprocess the device operation data and the physical quantity data of the device, which are acquired by the data acquisition and edge calculation module, for example, noise reduction, normalization, abnormal value processing, and operating condition segmentation may be performed on the signal data of the sensor. Then, a feature vector representing the signal can be obtained by a feature extraction module by using wavelet decomposition, wavelet packet decomposition, time domain statistical features, frequency spectrum peak features, time-frequency domain features and deep learning feature extraction methods such as SAE and CNN. Then, the feature effectiveness index calculation module is used for calculating the effectiveness index of the feature, the calculated comprehensive weight index is compared with a set threshold value, the feature of which the comprehensive weight index is smaller than the weight threshold value is omitted and then used as the effectiveness index, the decline trend of the equipment is evaluated, and therefore the residual service life of the equipment is predicted. And then, combining a spare part purchasing period and an equipment maintenance plan to accurately predict the spare part requirements.
Specifically, the intelligent analysis module may further include a data preprocessing module, a feature extraction module, a feature validity index calculation module, and a feature selection module.
The functions of the data preprocessing module mainly include but are not limited to denoising, normalizing, abnormal value processing, working condition segmentation and the like on the data of the sensor. For example, the data acquired by the data acquisition module may be filtered and analog-to-digital converted correspondingly by edge intelligent hardware, and the data acquisition frequency may be selected and configured according to actual requirements.
The function of the feature extraction module is mainly to obtain a feature vector representing the signal by a certain method, wherein the method includes but is not limited to wavelet decomposition, wavelet packet decomposition, time domain statistical features, frequency spectrum peak features, time-frequency domain features and deep learning feature extraction methods such as SAE and CNN.
The characteristic effectiveness index calculation module calculates the degeneration index, the monotonicity index, the robustness index and the consistency index of the characteristic respectively through the degeneration index calculation module, the monotonicity index calculation module, the robustness index calculation module and the consistency index calculation module, and obtains the comprehensive weight index through weighting calculation.
The feature selection module reserves the features of which the calculated comprehensive weight index is greater than or equal to a preset weight threshold, omits the features of which the comprehensive weight index is less than the weight threshold, and takes the residual features as the selected trend index.
In the present application, the method for calculating the feature validity index includes the following steps:
(1) acquiring operation data of equipment through a sensor, and preprocessing the acquired operation data to obtain full life cycle characteristic data of at least one group of equipment;
(2) utilizing a feature extraction method to obtain a plurality of feature vectors representing data signals;
(3) performing degradation index calculation, monotonicity index calculation, robustness index calculation and consistency index calculation on the plurality of feature vectors to respectively obtain a degradation index, a monotonicity index, a robustness index and a consistency index of each feature vector;
(4) setting a weight coefficient for the weight of each index, and obtaining a comprehensive weight index through weighting calculation;
(5) and comparing the comprehensive weight index obtained by calculation with a set weight threshold, and removing the characteristic that the comprehensive weight index is smaller than the weight threshold to be used as an effectiveness index.
In step (3), the specific methods of performing the degeneracy index calculation, monotonicity index calculation, robustness index calculation, and consistency index calculation on the plurality of feature vectors are described in detail below.
The calculation method of the degradation index calculation module comprises the following steps:
(1) before the trend feature weight calculation, a customized linear and nonlinear feature change Pattern (Pattern) needs to be designed, wherein the nonlinear feature change Pattern comprises a convex function and a concave function, and the convex function comprises y ═ xα(α＞1),y＝eαx(α > 0), and the like, and the concave function includes functions of y ═ log (α x), (α > 0), y ═ tanh (α x), (α > 0), and the like, and the concave function and the convex function need only be selected so as to satisfy the definitions thereof. For example, two kinds of concave-convex functions are selected and defined according to the following formula:
wherein x isiData of a time field, i is 1,2,3,. and n; to simplify the calculation, α is an integer.
(2) And respectively solving correlation coefficients between the extracted feature vectors and different feature change modes, wherein a correlation solving method can adopt Pearson and other methods.
(3) Defining the coefficient when the correlation coefficient is maximum as the degradation index of the feature:
Trendability＝max(corr(F,Pα) Equation (2)
Wherein F is a feature vector, PαThe characteristic change modes under different parameters alpha.
(4) When there are M (M >1) groups of full lifecycle data, then to meet the consistency requirement, definitions are defined
Wherein TrendmSign is the sign function for the degradation index (M1, 2, …, M) of the mth full life cycle sample of the feature.
The calculation method of the monotonicity index calculation module comprises the following steps:
(1) performing sliding window processing on the features, fitting the data of each window, performing smoothing processing, and then obtaining the average slope k of the data of the ith windowi。
(2) Monotonicity index is defined as
Wherein k isiThe resulting slope is fitted to the data for the ith window.
(3) When M (M >1) groups of full life cycle data exist, in order to meet the consistency requirement, taking the minimum monotonicity index in a plurality of groups of samples as the monotonicity index of the whole, and defining as follows:
monotonicity＝min(monm) M1, 2, 1, M equation (5)
Wherein monmThe monotonicity index of the mth group of full life cycle characteristic data is M, which is 1,2, … and M.
The calculation method of the robustness index calculation module comprises the following steps:
(1) fitting the characteristic data, wherein the fitting method is not limited, and smoothing can be performed to obtain the fitted or smoothed characteristic Yk(k ═ 1, 2.., N), original features noted Xk(k＝1,2,...,N)。
(2) Calculating a robustness index:
(3) when M (M >1) groups of full life cycle data exist, in order to meet the consistency requirement, taking the minimum robustness index in the multiple groups of samples as the overall robustness index, and defining:
Robustness＝min(robm) M1, 2, 1, M equation (7)
Wherein, robmThe M is a robustness index of the mth group of full life cycle feature data, and M is 1,2, … and M.
The calculation method of the consistency index calculation module comprises the following steps:
(1) fitting the characteristic data to obtain a fitted characteristic Yk(k-1, 2.., N), define FT-YN，ST＝Y1；
(2) When M groups (M >1) of full life cycle feature data exist, a consistency index is calculated:
wherein M is 1,2, …, M.
The processing flow of the comprehensive index calculation module comprises the following steps:
(1) when only one set of full life cycle feature data exists, the comprehensive weight index is defined as:
Feature_effective_Index＝ω1*trend+ω2*mon+ω3*rob
wherein, trend is a degeneration index, mon is a monotonicity index, rob is a stability index, omega isi(i is 1,2,3) is a weight of each index.
(2) When a plurality of groups of full life cycle characteristic data exist, defining the comprehensive weight index as follows:
Feature_effective_Index＝ω1*trend+ω2*mon+ω3*rob+ω4*con
where trend is a degeneration index, mon is a monotonicity index, rob is a stability index, con is a consistency index, ω isi(i ═ 1,2,3,4) as the weight of each index, ωiThe setting of (b) can be adjusted according to the actual situation. For example, if the modeling process only focuses on the degradation of a feature, then ω can be set1＝1,ω2＝0,ω3＝0,ω40, can be represented by ωiTo select the type of features required for modeling.
The method for selecting the trend characteristics can effectively evaluate linear trend characteristics and nonlinear characteristics, has strong anti-noise capability of evaluation indexes, and reduces the phenomenon of unstable indexes caused by random errors through fitting the characteristics in characteristic consistency evaluation. In addition, the method can also detect the mutation of the characteristics, and provides a basis for selecting a predictive modeling method.
Visualization application module
The visual application module can carry out related visual interface design according to the actual requirements of the user, and the display information comprises: the system comprises basic equipment information, equipment health states, equipment fault diagnosis results, equipment key part health states, residual service lives, equipment predictive maintenance suggestions, spare part demand prediction results and spare part inventory information, so that a user can purchase and replace spare parts in time, and the optimal management of the spare part inventory is realized.
Examples
In this embodiment, the equipment spare part demand prediction system based on fault prediction and health management of the present application is used for demand prediction of a main shaft spare part of a numerical control machine tool in a manufacturing plant. The main shaft is a core component of a numerical control machine tool, is generally expensive, and once the main shaft is damaged, the maintenance, replacement or purchase period is long, so that the machine tool is in a long-time fault shutdown, and the production plan is influenced. Through carrying out failure prediction and health management to the main shaft, the abnormal conditions of main shaft can be detected in time, the spare part demand of main shaft is predicted accurately, the stock is optimized rationally or the purchase is arranged in advance, the machine tool is prevented from being shut down for a long time. The specific implementation process is as follows:
data acquisition and edge calculation module
Setting a machine tool spindle variable-speed no-load test, mounting a vibration acceleration sensor on a spindle shell, and acquiring spindle vibration signals and machine tool controller signals during the spindle variable-speed no-load test by using edge intelligent hardware; running a data preprocessing program on edge intelligent hardware, dividing a main shaft vibration signal in real time according to rotating speed information in a machine tool controller signal and a preset segmentation time length to obtain a vibration signal with a plurality of segments of time length at each rotating speed, extracting the characteristics of each segment of vibration signal, and calculating a characteristic value; and uploading the characteristic value to a data service module.
Data service module
And data uploaded by the data acquisition and edge calculation module, data such as an equipment maintenance plan, work order information, a spare part purchasing period and the like in a factory MES system are accessed and integrated to the data service module for unified storage and management, and a data interface is provided to transmit the data to the intelligent analysis module for modeling and displaying by the visual application module.
Intelligent analysis module
The method comprises the steps of carrying out model training by using a characteristic value sample accumulated by repeated machine tool spindle variable-speed no-load tests, establishing a spindle health condition baseline model by adopting a Principal Component Analysis (PCA) algorithm, and detecting spindle abnormality by calculating a residual error (SPE) of a model predicted value and a Hotelling T2 statistic (T2). The method can calculate the health degree and the residual service life of the main shafts of up to 50 numerical control machines and the risk coefficient of the machine tool, combine the maintenance schedule of the machine tool as input, establish a main shaft spare part demand accurate prediction model by taking the purchase period of the main shafts and the labor cost of maintenance personnel as constraint conditions, and calculate the spare part demand.
Visualization application module
The visualization application module displays state information and decision suggestions, and mainly comprises the following steps: overview interface and single machine tool interface. The overview interface can display the running state of all machine tools, the health state of the main shaft, a maintenance plan, the inventory of main shaft spare parts, the purchase period of the main shaft spare parts, the demand quantity of the spare parts and the purchase suggestion of the spare parts; the single machine tool interface can display basic information of a single machine tool, the running state of the machine tool, health indexes of a main shaft, service life trend and maintenance decision suggestions.
Although the embodiments disclosed in the present application are described above, the descriptions are only for the convenience of understanding the present application, and are not intended to limit the present application. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims.
Claims (10)
1. Equipment spare part demand prediction system based on fault prediction and health management, characterized in that the prediction system includes:
the data acquisition and edge calculation module acquires data by using edge equipment and extracts features required by modeling by analyzing and calculating the data;
the data service module is used for uniformly managing data assets of the field equipment and the system and providing data support for the intelligent analysis model;
the intelligent analysis module is used for carrying out signal processing and characteristic processing on the acquired data and carrying out health assessment according to the processed signal data;
and the visual application module is used for visually displaying the results of the fault prediction and the health assessment.
2. The system of claim 1, wherein the data acquisition and edge calculation module is capable of data aligning the acquired data with time stamps and then processing the acquired signal data to calculate and extract features required for modeling in the edge device by intelligent modeling analysis.
3. The equipment spare part demand forecasting system of claim 1 or 2, wherein the data service module is provided with a multi-source data interface capable of accessing data from the data acquisition and edge calculation module and data of an existing system.
4. The equipment spare part demand prediction system of claim 1 or 2, wherein the intelligent analysis module multiplies the calculated equipment health index and key component health index values by risk coefficients of the equipment and key components respectively and performs weighting calculation to obtain the priority of equipment spare part replacement.
5. The equipment spare part demand prediction system of claim 4, wherein the intelligent analysis module builds a spare part demand prediction model to predict minimum spare part inventory in conjunction with equipment maintenance plans, equipment critical component failure probabilities, and remaining useful life.
6. The equipment spare part demand forecasting system of any one of claims 1 to 5, wherein the intelligent analysis module further comprises a data preprocessing module, a feature extraction module, a feature validity index calculation module, and a feature selection module.
7. The equipment spare part demand forecasting system of claim 6, wherein the feature effectiveness index calculating module calculates a degradation index, a monotonicity index, a robustness index and a consistency index of the feature through the degradation index calculating module, the monotonicity index calculating module, the robustness index calculating module and the consistency index calculating module, respectively, and obtains a comprehensive weight index through weighting calculation.
8. The equipment spare part demand forecasting system of claim 7, wherein the feature selection module reserves features with the calculated comprehensive weight index greater than or equal to a preset weight threshold, discards features with the comprehensive weight index less than the weight threshold, and takes the remaining features as the selected trending index.
9. The equipment spare part demand forecasting system of claim 6, wherein the method of performing feature validity index calculations comprises the steps of:
(1) acquiring operation data of equipment through a sensor, and preprocessing the acquired operation data to obtain full life cycle characteristic data of at least one group of equipment;
(2) utilizing a feature extraction method to obtain a plurality of feature vectors representing data signals;
(3) performing degradation index calculation, monotonicity index calculation, robustness index calculation and consistency index calculation on the plurality of feature vectors to respectively obtain a degradation index, a monotonicity index, a robustness index and a consistency index of each feature vector;
(4) setting a weight coefficient for the weight of each index, and obtaining a comprehensive weight index through weighting calculation;
(5) and comparing the comprehensive weight index obtained by calculation with a set weight threshold, and removing the characteristic that the comprehensive weight index is smaller than the weight threshold to be used as an effectiveness index.
10. The equipment spare part demand forecasting system of any one of claims 7 to 9, wherein:
the data preprocessing module carries out filtering and analog-to-digital conversion processing on the acquired data; the feature extraction module obtains a feature vector representing the signal by a feature extraction method.
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