Fault monitoring method and system for electrical elements of mixing station

Abstract

The invention relates to the technical field of industrial monitoring and intelligent fault diagnosis. The invention provides a fault monitoring method and system for electrical elements of a mixing station. The method comprises the following steps: collecting operation signals of electrical elements of the mixing station; performing signal anti-interference processing based on the operation signal to obtain a preprocessed signal; according to the pre-processing signal, fault characteristic parameters of electrical elements of the mixing station are extracted, and the fault characteristic parameters comprise voltage reduction starting conversion logic characteristics of a contactor and overload transient characteristics of a motor; based on the fault characteristic parameters, fault trend analysis is carried out, and element performance degradation trend and potential fault risk are identified; and according to a result of the fault trend analysis, generating a multi-stage early warning signal for triggering preventive maintenance operation. The problems of failure of fault feature extraction, signal distortion and lack of potential risk early warning caused by insufficient pertinence, weak anti-interference capability and low intelligent degree of an existing mixing station electrical element monitoring technology are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial monitoring and intelligent fault diagnosis, and in particular to a fault monitoring method and system for electrical components of a mixing station. Background Art

[0002] In the construction industry, mixing plants are critical facilities for producing materials like concrete. Their stable and efficient operation relies on the proper functioning of numerous electrical components, such as motors, contactors, relays, and sensors. However, these components are often exposed to harsh operating conditions characterized by high dust levels, strong vibrations, and complex electromagnetic interference, leading to frequent failures.

[0003] Existing industrial monitoring technologies (such as SCADA systems) have been applied to some equipment monitoring, but they still have obvious limitations in monitoring electrical component failures in mixing plants: Lack of specificity: the general system does not fully consider the operating characteristics and failure modes of specific electrical components in the mixing plant (such as the AC contactor step-down start-up conversion logic and motor overload transient characteristics), making it difficult to extract effective fault characteristic parameters; Weak anti-interference ability. The strong electromagnetic environment of the mixing station easily causes the collected voltage, current and other signals to be interfered with by power frequency and high-frequency noise. The traditional single filtering method is difficult to ensure signal quality, affecting the accuracy of subsequent analysis. The level of intelligence is low. Existing solutions are mostly based on simple threshold alarms, lacking intelligent analysis of operating data trends and multi-level early warning mechanisms. They are unable to effectively identify potential failure risks (such as slow degradation of component performance) and find it difficult to provide data support for preventive maintenance. Summary of the Invention

[0004] The purpose of the present invention is to provide a fault monitoring method and system for electrical components of mixing plants, aiming to solve the problems of failure in fault feature extraction, signal distortion and lack of potential risk warning caused by the existing monitoring technology of electrical components of mixing plants due to insufficient targeting, weak anti-interference ability and low intelligence level.

[0005] The present invention is achieved through the following technical solutions: A method for monitoring faults of electrical components in a mixing plant comprises the following steps: Collecting operating signals of electrical components of the mixing plant, wherein the operating signals include voltage signals and current signals; Based on the operating signal, signal anti-interference processing is performed to suppress power frequency interference and high-frequency noise pollution to obtain a preprocessed signal; Extracting fault characteristic parameters of electrical components of the mixing plant based on the preprocessed signals, wherein the fault characteristic parameters include the voltage reduction start-up conversion logic characteristics of the contactor and the overload transient characteristics of the motor; Based on the fault characteristic parameters, perform fault trend analysis to identify component performance degradation trends and potential failure risks; Based on the results of the fault trend analysis, a multi-level early warning signal is generated to trigger a preventive maintenance operation.

[0006] Optionally, the specific process of collecting the operating signals of the electrical components of the mixing station, wherein the operating signals include voltage signals and current signals, is as follows: Configure corresponding data acquisition modules for the AC contactors and motor core electrical components in the mixing plant; The voltage signal of the electrical component is isolated and sensed through a voltage transformer, and a true RMS measurement chip is used to continuously collect the true RMS and peak value of the voltage waveform in real time; The current signal is collected by the current sensor, and the voltage signal and the current signal are transmitted to the signal processing end via the RS-485 bus interface according to the MODBUS-RTU protocol.

[0007] Optionally, the specific process of performing signal anti-interference processing based on the operating signal to suppress power frequency interference and high-frequency noise pollution to obtain the preprocessed signal is: Receive voltage and current signals from the RS-485 bus interface; Performing signal conditioning on the voltage signal and the current signal, including amplification and baseline correction, to eliminate offset errors introduced by the sensor; Adaptive band-stop filters are used to suppress power frequency interference, with the center frequency dynamically adjusted to the power frequency of the grid; Use wavelet threshold denoising filter to suppress high-frequency noise pollution, and remove high-frequency noise components through wavelet decomposition and reconstruction; The filtered signal is processed by sliding average to smooth transient interference and obtain a stable preprocessed signal.

[0008] Optionally, the specific process of extracting the fault characteristic parameters of the electrical components of the mixing station according to the preprocessed signal is: During the contactor startup phase, the current waveform in the preprocessed signal is divided into time windows to identify the action timing of the reduced-voltage starting contactor and the running contactor. The current conversion time difference between the reduced-voltage startup phase and the full-voltage operation phase is calculated, and the logical interlock status of the contactor contact action signals in the two phases is detected. If the conversion time difference exceeds a preset threshold or a logical interlock failure is detected, it is extracted as a conversion timeout feature parameter or a logical conflict feature parameter, respectively. Monitor the effective value of the motor current in the preprocessed signal in real time; capture instantaneous sudden increase events in the effective current value and record the sudden increase amplitude and duration; calculate the energy integral or equivalent thermal effect of the sudden increase event; if the sudden increase amplitude exceeds the set multiple of the rated current, the duration is greater than the minimum overload maintenance time, or the energy integral exceeds the safety threshold, then extract them as transient overload amplitude characteristic parameters, overload duration characteristic parameters, or overload energy characteristic parameters respectively.

[0009] Optionally, the specific process of performing fault trend analysis based on the fault characteristic parameters to identify component performance degradation trends and potential fault risks is as follows: Periodically collecting the conversion timeout characteristic parameters, logic conflict characteristic parameters, transient overload amplitude characteristic parameters, overload duration characteristic parameters, and overload energy characteristic parameters to construct a time series characteristic database; Count the triggering frequency of the conversion timeout characteristic parameter within a preset period and calculate the sliding change rate of the mean time difference of the triggering frequency; detect the continuous occurrence period of the logic conflict characteristic parameter and associate it with the number of corresponding contactor operations; if the monthly change rate of the mean conversion time difference exceeds the monthly change threshold or the weekly increase in the logic conflict frequency exceeds the weekly increase threshold, it is determined that the contactor contacts are worn and degraded; Exponential smoothing is performed on the transient overload amplitude characteristic parameters to extract the long-term rising slope of the overload event amplitude. The overload duration characteristic parameters and the overload energy characteristic parameters are aggregated to calculate the equivalent cumulative thermal stress coefficient. If the amplitude rising slope is higher than the baseline value by two standard deviations for three consecutive cycles, or the cumulative thermal stress coefficient exceeds the insulation aging threshold, the motor winding performance is determined to be degraded.

[0010] Optionally, the specific process of generating a multi-level warning signal based on the result of the fault trend analysis to trigger a preventive maintenance operation is as follows: Based on the wear and degradation of the contactor contacts or the performance degradation of the motor windings identified in the fault trend analysis, a hierarchical early warning decision is made: When the contactor contact wear and degradation state is identified, a first-level warning signal is generated, triggering the contactor contact lubrication maintenance instruction; if the contact wear and degradation state continues to deteriorate and the logic conflict frequency exceeds the safety threshold, it is upgraded to a second-level warning signal, triggering the contactor replacement operation instruction; When motor winding performance degradation is detected, a level 1 warning signal is generated, triggering instructions for motor load optimization and cooling system inspection. If the equivalent cumulative thermal stress coefficient continues to exceed the insulation aging threshold and the amplitude increase slope reaches the critical slope, it is upgraded to a level 3 warning signal, triggering instructions for motor winding insulation reinforcement or replacement. Multi-level warning signals are pushed to the remote operation and maintenance platform through the industrial Internet of Things gateway, and the local sound and light alarm devices of the mixing station are activated simultaneously.

[0011] Optionally, the fault trend analysis includes: based on the fault characteristic parameters, using a pre-trained machine learning model to intelligently predict the component performance degradation trend and potential failure risk; wherein, the machine learning model is trained through historical fault characteristic parameter data, used to optimize fault identification accuracy, and output prediction results to undertake the generation of the multi-level warning signal.

[0012] Based on the same inventive concept, the present invention also provides a fault monitoring system for electrical components of a mixing plant, which is used to implement the fault monitoring method for electrical components of a mixing plant, comprising: The signal acquisition module is used to configure corresponding data acquisition modules for the AC contactors and motor core electrical components in the mixing plant. The voltage signals of the electrical components are isolated and sensed through voltage transformers. The true RMS measurement chip is used to continuously collect the true RMS value and peak value of the voltage waveform in real time. The current signal is collected through the current sensor. The voltage and current signals are then transmitted to the signal processing end via the RS-485 bus interface according to the MODBUS-RTU protocol; A signal anti-interference processing module is used to receive voltage and current signals from the RS-485 bus interface, perform signal conditioning on the voltage and current signals, including amplification and baseline correction, eliminate offset errors introduced by the sensor, use an adaptive band-stop filter to suppress power frequency interference, and dynamically adjust the center frequency to the power grid frequency. A wavelet threshold denoising filter is used to suppress high-frequency noise pollution, and high-frequency noise components are removed through wavelet decomposition and reconstruction. A sliding average process is performed on the filtered signal to smooth transient interference and obtain a stable pre-processed signal. A fault characteristic parameter extraction module is used to divide the current waveform in the preprocessing signal into time windows during the contactor startup phase, identify the action timing of the step-down starting contactor and the running contactor, calculate the current conversion time difference between the step-down starting phase and the full-voltage operation phase, and detect the logical interlocking state of the contactor contact action signal in the two phases. If the conversion time difference exceeds a preset threshold or a logic interlock failure is detected, it is extracted as a conversion timeout characteristic parameter or a logic conflict characteristic parameter. At the same time, the effective value of the motor current in the preprocessing signal is monitored in real time, the instantaneous sudden increase event of the effective value of the current is captured, the sudden increase amplitude and duration are recorded, and the energy integral or equivalent thermal effect of the sudden increase event is calculated. If the sudden increase amplitude exceeds the set multiple of the rated current, the duration is greater than the minimum overload maintenance time, or the energy integral exceeds the safety threshold, it is extracted as a transient overload amplitude characteristic parameter, an overload duration characteristic parameter, or an overload energy characteristic parameter. A fault trend analysis module is used to periodically collect the conversion timeout characteristic parameters, logic conflict characteristic parameters, transient overload amplitude characteristic parameters, overload duration characteristic parameters, and overload energy characteristic parameters, build a time series characteristic database, count the triggering frequency of the conversion timeout characteristic parameters within a preset period, calculate the sliding change rate of the time difference mean of the triggering frequency, detect the continuous occurrence period of the logic conflict characteristic parameters, and associate the number of actions of the corresponding contactor. If the monthly change rate of the conversion time difference mean exceeds the monthly change threshold or the weekly increase in the logic conflict frequency is greater than the weekly increase threshold, it is determined that the contactor contact is worn and degraded. The transient overload amplitude characteristic parameters are exponentially smoothed to extract the long-term rising slope of the overload event amplitude, aggregate the overload duration characteristic parameters and the overload energy characteristic parameters, and calculate the equivalent cumulative thermal stress coefficient. If the amplitude rising slope is higher than 2 times the standard deviation of the baseline value for three consecutive cycles, or the cumulative thermal stress coefficient exceeds the insulation aging threshold, it is determined that the motor winding performance is degraded. Based on the fault characteristic parameters, a pre-trained machine learning model is used to intelligently predict the component performance degradation trend and potential failure risk; The multi-level warning signal generation module is used to execute hierarchical warning decisions based on the contactor contact wear and degradation status or motor winding performance degradation status identified in the fault trend analysis. When the contactor contact wear and degradation status is identified, a first-level warning signal is generated to trigger the contactor contact lubrication maintenance instruction. If the contact wear and degradation status continues to deteriorate and the frequency of logical conflicts exceeds the safety threshold, it is upgraded to a second-level warning signal to trigger the contactor replacement operation instruction. When the motor winding performance degradation status is identified, a first-level warning signal is generated to trigger the motor load optimization and heat dissipation system inspection instruction. If the equivalent cumulative thermal stress coefficient continues to exceed the insulation aging threshold and the amplitude rising slope reaches the critical slope, it is upgraded to a third-level warning signal to trigger the motor winding insulation reinforcement or replacement operation instruction. The multi-level warning signal is pushed to the remote operation and maintenance platform through the industrial Internet of Things gateway, and the local sound and light alarm device of the mixing station is simultaneously activated.

[0013] Based on the same inventive concept, the present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned fault monitoring method for electrical components of a mixing plant.

[0014] Based on the same inventive concept, the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the above-mentioned fault monitoring method for electrical components of a mixing plant is implemented.

[0015] The technical solution of the present invention has at least the following advantages and beneficial effects: Based on the operating characteristics and fault modes of specific electrical components in the mixing plant, the fault characteristic parameters such as the contactor's step-down starting conversion logic characteristics and the motor's overload transient characteristics are accurately extracted. Compared with general industrial monitoring systems, this system can better fit the actual operating conditions of the mixing plant's electrical components, effectively overcome the problem of insufficient monitoring targeting in existing technologies, and significantly improve the effectiveness and accuracy of fault feature extraction.

[0016] By performing special anti-interference processing on the collected operating signals such as voltage and current, the power frequency interference and high-frequency noise pollution in the strong electromagnetic environment of the mixing station can be effectively suppressed. Compared with the traditional single filtering method, it can better ensure signal quality, avoid analysis errors caused by signal interference, and provide a more reliable data basis for subsequent fault analysis.

[0017] It breaks through the limitations of the simple threshold alarm of the existing solution and performs fault trend analysis based on the extracted fault characteristic parameters. It can effectively identify the performance degradation trend of components and the risk of potential failures, and generate multi-level early warning signals. It can realize in-depth intelligent analysis of the operating status of electrical components, and provide strong data support for preventive maintenance. It changes the previous dilemma of difficult to detect potential faults in advance, reduces the probability of sudden equipment failures, ensures the stable and efficient operation of the mixing plant, and reduces the economic losses and construction delays caused by downtime due to failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 1 is a flow chart of a method for monitoring faults of electrical components in a mixing plant according to an embodiment of the present invention; Figure 2 Schematic diagram of the structure of a fault monitoring system for electrical components in a mixing plant according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following is a specific implementation method with reference to the accompanying drawings.

[0020] Reference Figure 1 A method for monitoring faults of electrical components in a mixing plant comprises the following steps: Step 1: Collect operating signals of electrical components of the mixing station, wherein the operating signals include voltage signals and current signals.

[0021] In some embodiments, the specific process of collecting the operating signals of the electrical components of the mixing plant, wherein the operating signals include voltage signals and current signals, is as follows: Configure corresponding data acquisition modules for the AC contactors and motor core electrical components in the mixing plant; The voltage signal of the electrical component is isolated and sensed through a voltage transformer, and a true RMS measurement chip is used to continuously collect the true RMS and peak value of the voltage waveform in real time; The current signal is collected by the current sensor, and the voltage signal and the current signal are transmitted to the signal processing end via the RS-485 bus interface according to the MODBUS-RTU protocol.

[0022] Step 2: Based on the operating signal, perform signal anti-interference processing to suppress power frequency interference and high-frequency noise pollution to obtain a preprocessed signal.

[0023] In some embodiments, the specific process of performing signal anti-interference processing based on the operating signal to suppress power frequency interference and high-frequency noise pollution to obtain the preprocessed signal is: Receive voltage and current signals from the RS-485 bus interface; Performing signal conditioning on the voltage signal and the current signal, including amplification and baseline correction, to eliminate offset errors introduced by the sensor; Adaptive band-stop filters are used to suppress power frequency interference, with the center frequency dynamically adjusted to the power frequency of the grid; Use wavelet threshold denoising filter to suppress high-frequency noise pollution, and remove high-frequency noise components through wavelet decomposition and reconstruction; The filtered signal is processed by sliding average to smooth transient interference and obtain a stable preprocessed signal.

[0024] Step 3: Extract the fault characteristic parameters of the electrical components of the mixing plant based on the preprocessed signal, wherein the fault characteristic parameters include the voltage reduction start-up conversion logic characteristics of the contactor and the overload transient characteristics of the motor.

[0025] In some embodiments, the specific process of extracting the fault characteristic parameters of the electrical components of the mixing station based on the preprocessed signal is: During the contactor startup phase, the current waveform in the preprocessed signal is divided into time windows to identify the action timing of the reduced-voltage starting contactor and the running contactor. The current conversion time difference between the reduced-voltage startup phase and the full-voltage operation phase is calculated, and the logical interlock status of the contactor contact action signals in the two phases is detected. If the conversion time difference exceeds a preset threshold or a logical interlock failure is detected, it is extracted as a conversion timeout feature parameter or a logical conflict feature parameter, respectively. Monitor the effective value of the motor current in the preprocessed signal in real time; capture instantaneous sudden increase events in the effective current value and record the sudden increase amplitude and duration; calculate the energy integral or equivalent thermal effect of the sudden increase event; if the sudden increase amplitude exceeds the set multiple of the rated current, the duration is greater than the minimum overload maintenance time, or the energy integral exceeds the safety threshold, then extract them as transient overload amplitude characteristic parameters, overload duration characteristic parameters, or overload energy characteristic parameters respectively.

[0026] Step 4: Based on the fault characteristic parameters, perform fault trend analysis to identify component performance degradation trends and potential fault risks.

[0027] In some embodiments, the specific process of performing fault trend analysis based on the fault characteristic parameters to identify component performance degradation trends and potential failure risks is as follows: Periodically collecting the conversion timeout characteristic parameters, logic conflict characteristic parameters, transient overload amplitude characteristic parameters, overload duration characteristic parameters, and overload energy characteristic parameters to construct a time series characteristic database; Count the triggering frequency of the conversion timeout characteristic parameter within a preset period and calculate the sliding change rate of the mean time difference of the triggering frequency; detect the continuous occurrence period of the logic conflict characteristic parameter and associate it with the number of corresponding contactor operations; if the monthly change rate of the mean conversion time difference exceeds the monthly change threshold or the weekly increase in the logic conflict frequency exceeds the weekly increase threshold, it is determined that the contactor contacts are worn and degraded; Exponential smoothing is performed on the transient overload amplitude characteristic parameters to extract the long-term rising slope of the overload event amplitude. The overload duration characteristic parameters and the overload energy characteristic parameters are aggregated to calculate the equivalent cumulative thermal stress coefficient. If the amplitude rising slope is higher than the baseline value by two standard deviations for three consecutive cycles, or the cumulative thermal stress coefficient exceeds the insulation aging threshold, the motor winding performance is determined to be degraded.

[0028] Step 5: Generate multi-level warning signals based on the results of the fault trend analysis to trigger preventive maintenance operations.

[0029] In some embodiments, the specific process of generating a multi-level warning signal based on the results of the fault trend analysis to trigger a preventive maintenance operation is as follows: Based on the wear and degradation of the contactor contacts or the performance degradation of the motor windings identified in the fault trend analysis, a hierarchical early warning decision is made: When the contactor contact wear and degradation state is identified, a first-level warning signal is generated, triggering the contactor contact lubrication maintenance instruction; if the contact wear and degradation state continues to deteriorate and the logic conflict frequency exceeds the safety threshold, it is upgraded to a second-level warning signal, triggering the contactor replacement operation instruction; When motor winding performance degradation is detected, a level 1 warning signal is generated, triggering instructions for motor load optimization and cooling system inspection. If the equivalent cumulative thermal stress coefficient continues to exceed the insulation aging threshold and the amplitude increase slope reaches the critical slope, it is upgraded to a level 3 warning signal, triggering instructions for motor winding insulation reinforcement or replacement. Multi-level warning signals are pushed to the remote operation and maintenance platform through the industrial Internet of Things gateway, and the local sound and light alarm devices of the mixing station are activated simultaneously.

[0030] In some embodiments, the fault trend analysis includes: based on the fault characteristic parameters, using a pre-trained machine learning model to intelligently predict the component performance degradation trend and potential failure risk; wherein, the machine learning model is trained through historical fault characteristic parameter data, used to optimize fault identification accuracy, and output prediction results to undertake the generation of the multi-level warning signal.

[0031] Historical fault characteristic parameter data can be collected from multiple mixing stations over a long period of time to form a time series data set. The time series data set includes: Contactor related characteristics: conversion timeout characteristic parameters (denoted as , in seconds), logical conflict characteristic parameters (recorded as , which is a binary value or a frequency count); Motor-related characteristics: transient overload amplitude characteristic parameters (denoted as , in amperes), overload duration characteristic parameter (denoted as , in seconds), overload energy characteristic parameters (denoted as , in joules); Label data: Based on actual maintenance records or expert diagnosis, the performance degradation status of components at each time point is annotated (as labels for supervised learning); labels use discrete levels: Contactor: 0 (normal), 1 (slight wear and degradation), 2 (severe wear and degradation); Motor: 0 (normal), 1 (slight insulation degradation), 2 (severe insulation degradation).

[0032] Feature parameters are collected at fixed periods (such as daily or weekly) to construct time series.

[0033] Convert time series data into supervised learning format. For each time point , using a sliding window to extract the past The feature sequence of periods is used as the model input. For example, the window size (representing data from the past 30 days), the input sequence is ,in, It's time The characteristic vector of (for example, for contactors, ; For motor, For simplicity, independent models can be trained for the contactor and motor to avoid feature coupling.

[0034] Feature parameters were z-score normalized to eliminate dimensionality effects. Historical data was divided into a training set (70%), a validation set (15%), and a test set (15%) in chronological order to ensure temporal integrity.

[0035] The Long Short-Term Memory (LSTM) network is used as the core machine learning model. LSTM is good at processing long-term dependencies in time series data and is suitable for capturing performance degradation trends (such as the gradual wear of contactor contacts or the cumulative thermal stress effects of motor windings). The model structure includes an input layer, an LSTM layer, a fully connected layer, and an output layer. The input layer receives a sliding window sequence. , the dimension is ,in, Represents the characteristic dimension. In this example, the contactor model , motor model The LSTM layer includes multiple LSTM unit processing sequences to extract time series features. Each LSTM unit contains a forget gate, an input gate, and an output gate, and can dynamically learn important information. The fully connected layer is used to convert the LSTM output into a high-level representation. The output layer uses a softmax activation function to output the predicted probability distribution of the degradation state. The degradation states include normal, slightly degraded, and severely degraded.

[0036] Use Categorical Cross-Entropy Loss to optimize the difference between the predicted label and the true label, as shown in the following formula (1):

[0037] in, Indicates the number of samples; Representation sample The true label (one-hot encoding); represents the sample predicted by the model Belong to category probability.

[0038] The Adam optimizer was used with a learning rate of 0.001 and early stopping to prevent overfitting. Training was stopped when the validation set loss did not decrease for 10 consecutive epochs. The loss function was minimized while monitoring the validation set accuracy. After training, the model was able to predict the probability of degradation in the next period (e.g., one day).

[0039] After step 3 is completed, the system obtains the latest fault characteristic parameters; updates the sliding window sequence (Remove the oldest data and add the latest data); input the pre-trained LSTM model and output the predicted probability distribution, that is, the predicted sample Belong to category Probability ; Generate degradation state prediction based on probability, take As the degradation level, that is, from the three degradation state probabilities predicted by the model, the state with the highest probability value is selected as the component performance degradation level for final determination.

[0040] The output of machine learning models can be used to supplement or optimize existing rules: When the model predicts a degradation level of 1 or 2, the model result is used first because it is data-driven and has higher accuracy; When the model prediction is uncertain, such as when the highest probability is less than 0.6, fall back to the original rule analysis in step 4, such as calculating the monthly change rate; The output prediction results (degradation level and probability) are directly input into step five to generate multi-level warning signals.

[0041] For example, the processing of a single LSTM unit is: Input: time step The eigenvector of (after normalization), the hidden state vector at the previous time step , the cell state vector at the previous time step .

[0042] The gate control mechanism is calculated as shown in the following formula (2):

[0043] in, Output vector for the forget gate; Output vector for the input gate; is the candidate cell state vector; is the current cell state vector; Output vector for the output gate; is the current hidden state vector; Represents the sigmoid function; represents the hyperbolic tangent function; represents element-wise multiplication; 、 、 and Represent the forget gate weight matrix, input gate weight matrix, candidate cell state weight matrix and output gate weight matrix respectively; 、 、 and They represent the forget gate bias vector, input gate bias vector, candidate cell state bias vector and output gate bias vector respectively.

[0044] Corresponding to the entire input sequence , the LSTM layer iteratively calculates all time steps to obtain the final hidden state .

[0045] The output layer calculation is shown in the following formula (3):

[0046] in, represents the weight matrix of the output layer; represents the bias vector of the output layer; Represents the logits vector after linear transformation; Represents the predicted probability distribution vector; represents the predicted probability of belonging to the category; represents the softmax activation function; Represents the category index; The first logits vector elements.

[0047] In step 5, the prediction results Directly used to trigger an early warning: Contactor early warning decision: If the predicted degradation level is 1 (slight wear degradation, probability greater than 0.6), a first-level warning signal is generated (triggering lubrication maintenance); If the predicted degradation level is 2 (severe wear degradation, probability greater than 0.8), and the logical conflict characteristic parameters If the safety threshold is exceeded (e.g. the frequency is greater than 5 times / week), it will be upgraded to a Level 2 warning signal (triggering replacement operation).

[0048] Motor early warning decision: If the predicted degradation level is 1 (slight insulation degradation, probability greater than 0.6), a level 1 warning signal is generated (triggering load optimization and heat dissipation checks); If the predicted degradation level is 2 (severe insulation degradation, probability greater than 0.8), and the overload energy characteristic parameter If the threshold is continuously exceeded, it will be upgraded to a level 3 warning signal (triggering insulation reinforcement or replacement).

[0049] Compared with pure rule-based approaches, this model provides more refined risk quantification through probabilistic output (for example, the probability value can be directly used as the basis for adjusting the warning threshold), and can issue warnings in the early stages of degradation (for example, when the probability slowly increases), thereby improving the timeliness of preventive maintenance.

[0050] Based on the same inventive concept, corresponding to any of the above embodiments, refer to Figure 2 The present invention provides a fault monitoring system for electrical components of a mixing plant, which is used to implement the aforementioned fault monitoring method for electrical components of a mixing plant, comprising: The signal acquisition module is used to configure corresponding data acquisition modules for the AC contactors and motor core electrical components in the mixing plant. The voltage signals of the electrical components are isolated and sensed through voltage transformers. The true RMS measurement chip is used to continuously collect the true RMS value and peak value of the voltage waveform in real time. The current signal is collected through the current sensor. The voltage and current signals are then transmitted to the signal processing end via the RS-485 bus interface according to the MODBUS-RTU protocol; A signal anti-interference processing module is used to receive voltage and current signals from the RS-485 bus interface, perform signal conditioning on the voltage and current signals, including amplification and baseline correction, eliminate offset errors introduced by the sensor, use an adaptive band-stop filter to suppress power frequency interference, and dynamically adjust the center frequency to the power grid frequency. A wavelet threshold denoising filter is used to suppress high-frequency noise pollution, and high-frequency noise components are removed through wavelet decomposition and reconstruction. A sliding average process is performed on the filtered signal to smooth transient interference and obtain a stable pre-processed signal. A fault characteristic parameter extraction module is used to divide the current waveform in the preprocessing signal into time windows during the contactor startup phase, identify the action timing of the step-down starting contactor and the running contactor, calculate the current conversion time difference between the step-down starting phase and the full-voltage operation phase, and detect the logical interlocking state of the contactor contact action signal in the two phases. If the conversion time difference exceeds a preset threshold or a logic interlock failure is detected, it is extracted as a conversion timeout characteristic parameter or a logic conflict characteristic parameter. At the same time, the effective value of the motor current in the preprocessing signal is monitored in real time, the instantaneous sudden increase event of the effective value of the current is captured, the sudden increase amplitude and duration are recorded, and the energy integral or equivalent thermal effect of the sudden increase event is calculated. If the sudden increase amplitude exceeds the set multiple of the rated current, the duration is greater than the minimum overload maintenance time, or the energy integral exceeds the safety threshold, it is extracted as a transient overload amplitude characteristic parameter, an overload duration characteristic parameter, or an overload energy characteristic parameter. A fault trend analysis module is used to periodically collect the conversion timeout characteristic parameters, logic conflict characteristic parameters, transient overload amplitude characteristic parameters, overload duration characteristic parameters and overload energy characteristic parameters, build a time series characteristic database, count the triggering frequency of the conversion timeout characteristic parameters within a preset period, calculate the sliding change rate of the time difference mean of the triggering frequency, detect the continuous occurrence period of the logic conflict characteristic parameters, and associate the number of actions of the corresponding contactor. If the monthly change rate of the conversion time difference mean exceeds the monthly change threshold or the weekly increase in the logic conflict frequency is greater than the weekly increase threshold, it is determined that the contactor contact is worn and degraded. The transient overload amplitude characteristic parameters are exponentially smoothed to extract the long-term rising slope of the overload event amplitude, aggregate the overload duration characteristic parameters and the overload energy characteristic parameters, and calculate the equivalent cumulative thermal stress coefficient. If the amplitude rising slope is higher than 2 times the standard deviation of the baseline value for three consecutive cycles, or the cumulative thermal stress coefficient exceeds the insulation aging threshold, it is determined that the motor winding performance is degraded. Based on the fault characteristic parameters, a pre-trained machine learning model can be used to intelligently predict the component performance degradation trend and potential failure risk; The multi-level warning signal generation module is used to execute hierarchical warning decisions based on the contactor contact wear and degradation status or motor winding performance degradation status identified in the fault trend analysis. When the contactor contact wear and degradation status is identified, a first-level warning signal is generated to trigger the contactor contact lubrication maintenance instruction. If the contact wear and degradation status continues to deteriorate and the frequency of logical conflicts exceeds the safety threshold, it is upgraded to a second-level warning signal to trigger the contactor replacement operation instruction. When the motor winding performance degradation status is identified, a first-level warning signal is generated to trigger the motor load optimization and heat dissipation system inspection instruction. If the equivalent cumulative thermal stress coefficient continues to exceed the insulation aging threshold and the amplitude rising slope reaches the critical slope, it is upgraded to a third-level warning signal to trigger the motor winding insulation reinforcement or replacement operation instruction. The multi-level warning signal is pushed to the remote operation and maintenance platform through the industrial Internet of Things gateway, and the local sound and light alarm device of the mixing station is simultaneously activated.

[0051] Based on the same inventive concept, corresponding to any of the above embodiments, the present invention provides an electronic device, including a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to enable the electronic device to execute the fault monitoring method for electrical components of a mixing plant of the embodiment.

[0052] Optionally, the above-mentioned electronic device may be a server.

[0053] In addition, this embodiment further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the fault monitoring method for electrical components of a mixing plant of the embodiment is implemented.

[0054] It is understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0055] The method steps in the embodiments of the present invention can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0056] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a storage medium or transmitted via a storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

Claims

1. A fault monitoring method for electrical components of a mixing plant, characterized in that: The following steps are involved: Collecting operating signals of electrical components of the mixing plant, wherein the operating signals include voltage signals and current signals; Based on the operating signal, signal anti-interference processing is performed to suppress power frequency interference and high-frequency noise pollution to obtain a preprocessed signal; Extracting fault characteristic parameters of electrical components of the mixing plant based on the preprocessed signals, wherein the fault characteristic parameters include the voltage reduction start-up conversion logic characteristics of the contactor and the overload transient characteristics of the motor; Based on the fault characteristic parameters, perform fault trend analysis to identify component performance degradation trends and potential failure risks; Based on the results of the fault trend analysis, a multi-level early warning signal is generated to trigger a preventive maintenance operation.

2. The fault monitoring method for electrical components of a mixing plant according to claim 1, characterized in that: The specific process of collecting the operating signals of the electrical components of the mixing station, which include voltage signals and current signals, is as follows: Configure corresponding data acquisition modules for the AC contactors and motor core electrical components in the mixing plant; The voltage signal of the electrical component is isolated and sensed through a voltage transformer, and a true RMS measurement chip is used to continuously collect the true RMS and peak value of the voltage waveform in real time; The current signal is collected by the current sensor, and the voltage signal and the current signal are transmitted to the signal processing end via the RS-485 bus interface according to the MODBUS-RTU protocol.

3. The fault monitoring method for electrical components of a mixing plant according to claim 2, characterized in that: The specific process of performing signal anti-interference processing based on the operating signal to suppress power frequency interference and high-frequency noise pollution to obtain the preprocessed signal is as follows: Receive voltage and current signals from the RS-485 bus interface; Performing signal conditioning on the voltage signal and the current signal, including amplification and baseline correction, to eliminate offset errors introduced by the sensor; Adaptive band-stop filters are used to suppress power frequency interference, with the center frequency dynamically adjusted to the power frequency of the grid; Use wavelet threshold denoising filter to suppress high-frequency noise pollution, and remove high-frequency noise components through wavelet decomposition and reconstruction; The filtered signal is processed by sliding average to smooth transient interference and obtain a stable preprocessed signal.

4. The fault monitoring method for electrical components of a mixing plant according to claim 1, characterized in that: The specific process of extracting the fault characteristic parameters of the mixing station electrical components based on the pre-processed signals is as follows: During the contactor startup phase, the current waveform in the pre-processed signal is divided into time windows to identify the action sequence of the reduced-voltage starting contactor and the running contactor. The current conversion time difference between the reduced-voltage startup phase and the full-voltage operation phase is calculated, and the logical interlocking state of the contactor contact action signal in the two phases is detected. If the conversion time difference exceeds a preset threshold or a logic interlock failure is detected, it is extracted as a conversion timeout characteristic parameter or a logic conflict characteristic parameter respectively; Real-time monitoring of the effective value of the motor current in the pre-processed signal; capturing the instantaneous sudden increase of the effective value of the current, and recording the sudden increase magnitude and duration; Calculate the energy integral or equivalent thermal effect of the sudden increase event; if the sudden increase exceeds the set multiple of the rated current, the duration is longer than the minimum overload maintenance time, or the energy integral exceeds the safety threshold, then extract it as the transient overload amplitude characteristic parameter, overload duration characteristic parameter, or overload energy characteristic parameter respectively.

5. The fault monitoring method for electrical components of a mixing plant according to claim 4, characterized in that: The specific process of performing fault trend analysis based on the fault characteristic parameters and identifying component performance degradation trends and potential fault risks is as follows: Periodically collecting the conversion timeout characteristic parameters, logic conflict characteristic parameters, transient overload amplitude characteristic parameters, overload duration characteristic parameters, and overload energy characteristic parameters to construct a time series characteristic database; Count the triggering frequency of the conversion timeout characteristic parameter within a preset period and calculate the sliding change rate of the mean time difference of the triggering frequency; detect the continuous occurrence period of the logic conflict characteristic parameter and associate it with the number of corresponding contactor operations; if the monthly change rate of the mean conversion time difference exceeds the monthly change threshold or the weekly increase in the logic conflict frequency exceeds the weekly increase threshold, it is determined that the contactor contacts are worn and degraded; Exponential smoothing is performed on the transient overload amplitude characteristic parameters to extract the long-term rising slope of the overload event amplitude; The overload duration characteristic parameters and the overload energy characteristic parameters are aggregated to calculate the equivalent cumulative thermal stress coefficient. If the amplitude rising slope is higher than the baseline value by 2 standard deviations for three consecutive cycles, or the cumulative thermal stress coefficient exceeds the insulation aging threshold, the motor winding performance is determined to be degraded.

6. The fault monitoring method for electrical components of a mixing plant according to claim 5, characterized in that: The specific process of generating a multi-level warning signal based on the results of the fault trend analysis to trigger a preventive maintenance operation is as follows: Based on the wear and degradation of the contactor contacts or the performance degradation of the motor windings identified in the fault trend analysis, a hierarchical early warning decision is made: When the contactor contact wear and degradation state is identified, a first-level warning signal is generated, triggering the contactor contact lubrication maintenance instruction; If the contact wear and degradation continues to worsen and the logic conflict frequency exceeds the safety threshold, it will be upgraded to a secondary warning signal, triggering the contactor replacement operation instruction; When motor winding performance degradation is detected, a level 1 warning signal is generated, triggering instructions for motor load optimization and cooling system inspection. If the equivalent cumulative thermal stress coefficient continues to exceed the insulation aging threshold and the amplitude increase slope reaches the critical slope, it is upgraded to a level 3 warning signal, triggering instructions for motor winding insulation reinforcement or replacement. The multi-level early warning signals are pushed to the remote operation and maintenance platform through the industrial Internet of Things gateway, and the local sound and light alarm devices of the mixing station are activated simultaneously.

7. The fault monitoring method for electrical components of a mixing plant according to claim 1, characterized in that: The fault trend analysis includes: based on the fault characteristic parameters, using a pre-trained machine learning model to intelligently predict the component performance degradation trend and potential failure risks; wherein, the machine learning model is trained through historical fault characteristic parameter data, used to optimize fault identification accuracy, and output prediction results to undertake the generation of the multi-level warning signal.

8. A fault monitoring system for electrical components of a mixing plant, used to implement the fault monitoring method for electrical components of a mixing plant according to any one of claims 1 to 7, characterized in that: include: The signal acquisition module is used to configure corresponding data acquisition modules for the AC contactors and motor core electrical components in the mixing plant. The voltage signals of the electrical components are isolated and sensed through voltage transformers. The true RMS measurement chip is used to continuously collect the true RMS value and peak value of the voltage waveform in real time. The current signal is collected through the current sensor. The voltage and current signals are then transmitted to the signal processing end via the RS-485 bus interface according to the MODBUS-RTU protocol; A signal anti-interference processing module is used to receive voltage and current signals from the RS-485 bus interface, perform signal conditioning on the voltage and current signals, including amplification and baseline correction, eliminate offset errors introduced by the sensor, use an adaptive band-stop filter to suppress power frequency interference, and dynamically adjust the center frequency to the power grid frequency. A wavelet threshold denoising filter is used to suppress high-frequency noise pollution, and high-frequency noise components are removed through wavelet decomposition and reconstruction. A sliding average process is performed on the filtered signal to smooth transient interference and obtain a stable pre-processed signal. A fault characteristic parameter extraction module is used to divide the current waveform in the preprocessing signal into time windows during the contactor startup phase, identify the action timing of the step-down starting contactor and the running contactor, calculate the current conversion time difference between the step-down starting phase and the full-voltage operation phase, and detect the logical interlocking state of the contactor contact action signal in the two phases. If the conversion time difference exceeds a preset threshold or a logic interlock failure is detected, it is extracted as a conversion timeout characteristic parameter or a logic conflict characteristic parameter. At the same time, the effective value of the motor current in the preprocessing signal is monitored in real time, the instantaneous sudden increase event of the effective value of the current is captured, the sudden increase amplitude and duration are recorded, and the energy integral or equivalent thermal effect of the sudden increase event is calculated. If the sudden increase amplitude exceeds the set multiple of the rated current, the duration is greater than the minimum overload maintenance time, or the energy integral exceeds the safety threshold, it is extracted as a transient overload amplitude characteristic parameter, an overload duration characteristic parameter, or an overload energy characteristic parameter. A fault trend analysis module is used to periodically collect the conversion timeout characteristic parameters, logic conflict characteristic parameters, transient overload amplitude characteristic parameters, overload duration characteristic parameters, and overload energy characteristic parameters, build a time series characteristic database, count the triggering frequency of the conversion timeout characteristic parameters within a preset period, calculate the sliding change rate of the time difference mean of the triggering frequency, detect the continuous occurrence period of the logic conflict characteristic parameters, and associate the number of actions of the corresponding contactor. If the monthly change rate of the conversion time difference mean exceeds the monthly change threshold or the weekly increase in the logic conflict frequency is greater than the weekly increase threshold, it is determined that the contactor contact is worn and degraded. The transient overload amplitude characteristic parameters are exponentially smoothed to extract the long-term rising slope of the overload event amplitude, aggregate the overload duration characteristic parameters and the overload energy characteristic parameters, and calculate the equivalent cumulative thermal stress coefficient. If the amplitude rising slope is higher than 2 times the standard deviation of the baseline value for three consecutive cycles, or the cumulative thermal stress coefficient exceeds the insulation aging threshold, it is determined that the motor winding performance is degraded. Based on the fault characteristic parameters, a pre-trained machine learning model is used to intelligently predict the component performance degradation trend and potential failure risk; The multi-level warning signal generation module is used to execute hierarchical warning decisions based on the contactor contact wear and degradation status or motor winding performance degradation status identified in the fault trend analysis. When the contactor contact wear and degradation status is identified, a first-level warning signal is generated to trigger the contactor contact lubrication maintenance instruction. If the contact wear and degradation status continues to deteriorate and the frequency of logical conflicts exceeds the safety threshold, it is upgraded to a second-level warning signal to trigger the contactor replacement operation instruction. When the motor winding performance degradation status is identified, a first-level warning signal is generated to trigger the motor load optimization and heat dissipation system inspection instruction. If the equivalent cumulative thermal stress coefficient continues to exceed the insulation aging threshold and the amplitude rising slope reaches the critical slope, it is upgraded to a third-level warning signal to trigger the motor winding insulation reinforcement or replacement operation instruction. The multi-level warning signal is pushed to the remote operation and maintenance platform through the industrial Internet of Things gateway, and the local sound and light alarm device of the mixing station is simultaneously activated.

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