A Fault Diagnosis Method and System for Drainage Facilities in the Water Internet of Things Environment

Abstract

The present invention relates to the technical field of fault diagnosis, and discloses a fault diagnosis method and system for drainage facilities in a water network environment. A fault diagnosis method for drainage facilities in a water network environment includes: installing sensors to obtain drainage facility information and shaft seal monitoring information; respectively performing feature engineering on the obtained drainage facility information and shaft seal monitoring information data to obtain frequency domain data and time-frequency data; constructing first time series data and second time series data according to the obtained frequency domain data, time-frequency data and original time series data; constructing a fault detection model, and inputting the constructed first time series data and second time series data into the fault detection model to output a fault detection result; by comprehensively monitoring the shaft seal while monitoring the sewage lift pump, the present invention can quickly and efficiently detect some faults with high potential hazards but difficult to monitor caused by cavitation, so as to perform timely maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and more specifically, to a method and system for diagnosing drainage facility faults in a water networking environment. Background Art

[0002] The sewage lifting pump station is mainly used to raise the water level of sewage to ensure that the sewage can flow smoothly in the drainage pipe system by gravity. When the sewage lifting pump is in operation, since the pressure at the impeller inlet is lower than the saturated vapor pressure of the liquid, part of the liquid in the sewage will vaporize to form bubbles. These bubbles will burst quickly when they flow to the area with higher pressure. At the moment the bubble bursts, the surrounding liquid rushes to the center of the bubble at a very high speed, generating a strong impact force.

[0003] The strong vibration and impact caused by cavitation are transmitted to the shaft seal, causing the sealing element of the shaft seal to bear additional uneven force. Under normal circumstances, the shaft seal can effectively prevent sewage leakage in the pump and external air from entering the pump body. However, under the influence of cavitation, the sealing element is frequently deformed by force, a small gap may appear between the fitting surfaces of the dynamic and static rings of the mechanical seal, and the packing of the packing seal may become loose and wear more severe. With the emergence of these problems, the sealing performance of the shaft seal gradually decreases, initially manifested as slight sewage dripping. If not handled in time, the leakage will become more and more serious, eventually leading to a large amount of sewage leakage, which not only pollutes the working environment, but also may cause the lubricating fluid in the pump to be diluted or washed away by sewage, causing bearings and other components to be damaged due to lack of lubrication, further affecting the normal operation of the pump.

[0004] Since the shaft seal itself is located in a relatively hidden part of the pump body, it has poor visibility and is difficult to directly observe its sealing status. For mechanical seals, small gap changes and wear of the sealing surface are difficult to detect by conventional means, and will only be discovered when leakage is obvious. The situation of packing seals is similar. The looseness and wear of the packing will not cause obvious changes in appearance in the early stage, and it is difficult to monitor the leakage amount, because in actual operation, a small amount of sewage dripping may be considered a normal phenomenon, which is not easy to attract enough attention, thus missing the opportunity for early repair, and then causing more serious failures. Summary of the invention

[0005] The present invention provides a drainage facility fault diagnosis method and system in a water network environment, which solves the technical problem in the related art that the initial performance of the fault is not obvious and the fault is difficult to find.

[0006] The present invention provides a drainage facility fault diagnosis method in a water networking environment, comprising:

[0007] Step 100: Install sensors to obtain the drainage facility information and shaft seal monitoring information for the previous consecutive n time steps including the current time step. The drainage facility information includes: electrical system information, mechanical system information, hydraulic system information, liquid level control system information, and water quality related information.

[0008] Step 200: Perform feature engineering on the obtained drainage facility information and shaft seal monitoring information data for n time steps respectively to obtain frequency domain data and time-frequency data.

[0009] Step 300: Construct the first time series data and the second time series data based on the obtained frequency domain data and time-frequency data combined with the original time series data.

[0010] Step 400: Construct a fault detection model, and input the constructed first time series data and second time series data into the fault detection model to output the fault detection result.

[0011] The specific execution process of Step 400 is as follows:

[0012] Step 401: Use the constructed first time series data and second time series data as inputs to output the first hidden state and the second hidden state.

[0013] Step 402: Perform hidden state fusion on the first hidden state and the second hidden state to output the fused hidden state.

[0014] Step 403: Map the fused hidden state through the first fully connected layer to the output to obtain the fault detection result.

[0015] In a preferred embodiment, the electrical system information includes: motor voltage, motor current, insulation resistance value, control element action time.

[0016] The mechanical system information includes: vibration acceleration in horizontal, vertical and axial directions, vibration spectrum, temperature, noise decibel value.

[0017] The hydraulic system information includes: inlet flow rate, outlet flow rate, inlet pressure value, outlet pressure value.

[0018] The liquid level control system information includes: liquid level measurement value, liquid level change rate.

[0019] The water quality related information includes: pH value, sediment content, chemical oxygen demand (COD).

[0020] The shaft seal monitoring information includes: shaft seal temperature, seal cavity pressure, shaft seal vibration frequency.

[0021] In a preferred embodiment, the data acquisition frequency is fixed, and the default value is to acquire data once every 1 minute.

[0022] In a preferred embodiment, both the first time-series data and the second time-series data are used to obtain the first hidden state and the second hidden state through CNN and LSTM.

[0023] In a preferred embodiment, the output fault detection result is a vector, and the th component of the vector represents the probability value of the

[0024] th fault. If the probability value is higher than 0.5, it indicates that the fault has occurred; if not, it indicates normal.

[0025] In a preferred embodiment, the second hidden state is connected to the second fully connected layer for separate pre-training, and the output of the second fully connected layer is the fault state of the shaft seal; the fault states of the shaft seal include: normal, slight, moderate, and severe.

[0026] ;

[0027] : The number of samples in the training set for each iteration;

[0028] : The number of fault categories;

[0029] : The sample in the category true label;

[0030] : The model's predicted probability that the sample belongs to the category ;

[0031] : The logarithm of the predicted probability, used to calculate the difference between the prediction and the true label;

[0032] : The average cross-entropy loss value of the entire batch of samples.

[0033] In a preferred embodiment, feature engineering includes frequency-domain feature extraction and time-frequency feature extraction.

[0034] In a preferred embodiment, frequency-domain feature extraction: Apply the fast Fourier transform to each time-series data to convert it into frequency-domain features:

[0035] ;

[0036] : Original time series data;

[0037] : Fourier transform;

[0038] : Frequency domain data;

[0039] Time-frequency feature extraction: Apply wavelet transform to each time series data to extract time-frequency features:

[0040] ;

[0041] : Original time series data;

[0042] : Wavelet transform;

[0043] : Time-frequency data.

[0044] The beneficial effects of the present invention are as follows: By comprehensively monitoring the shaft seal while monitoring the sewage lift pump, the present invention can quickly and efficiently detect some faults with high potential hazards but difficult to monitor during cavitation, so as to carry out timely maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a method for diagnosing faults of drainage facilities in a water network environment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] Now, the subject matter described herein will be discussed with reference to example embodiments. It should be understood that discussing these embodiments is only to enable those skilled in the art to better understand and thus implement the subject matter described herein, and the functions and arrangements of the elements discussed can be changed without departing from the protection scope of the content of this specification. Each example can omit, substitute, or add various processes or components as needed. Additionally, the features described in some examples can also be combined in other examples.

[0047] In at least one embodiment of the present invention, a method for diagnosing faults of drainage facilities in a water network environment is disclosed, as Figure 1 shown, including:

[0048] Step 100, install sensors to obtain drainage facility information and shaft seal monitoring information for the previous consecutive n time steps including the current time step, where the drainage facility information includes: electrical system information, mechanical system information, hydraulic system information, liquid level control system information, water quality related information;

[0049] The electrical system information includes: motor voltage, motor current, insulation resistance value, control element operation time;

[0050] The mechanical system information includes: vibration acceleration in the horizontal, vertical and axial directions, vibration spectrum, temperature, noise decibel value;

[0051] The hydraulic system information includes: inlet flow rate, outlet flow rate, inlet pressure value, outlet pressure value;

[0052] The liquid level control system information includes: liquid level measurement value, liquid level change rate;

[0053] The water quality related information includes: pH value, sediment content, chemical oxygen demand (COD);

[0054] The shaft seal monitoring information includes: shaft seal temperature, seal cavity pressure, shaft seal vibration frequency;

[0055] In some embodiments of the present invention, high-precision vibration sensors are installed on the water pump to measure the vibration acceleration of the water pump in three axial directions (X, Y, Z), with the unit of m / s²; the temperature sensor uses a platinum resistance temperature sensor to measure the temperature of the water pump motor winding, with an accuracy of up to ±0.1°C and a range of 0 - 150°C; the pressure sensor selects a piezoresistive pressure sensor and is installed at the inlet and outlet of the water pump to measure the inlet and outlet pressures, with the unit of Pa and an accuracy of ±0.01%FS. An ultrasonic liquid level sensor is installed in the sump to measure the liquid level height, with a resolution of up to 1mm, and the measurement range depends on the depth of the sump. The output signal is a 4 - 20mA current signal corresponding to the liquid level range. An electromagnetic flowmeter is installed on the pipeline to measure the sewage flow rate, with an accuracy of ±0.5%, and the flow rate unit is m³ / h, and the flow velocity and flow rate data of the sewage in the pipeline can be obtained in real time.

[0056] The data acquisition frequency is fixed, and the default value is to acquire data once every 1 minute.

[0057] Step 200, perform feature engineering on the obtained drainage facility information and shaft seal monitoring information data for n time steps respectively to obtain frequency domain data and time-frequency data;

[0058] In an embodiment of the present invention, the feature engineering includes frequency domain feature extraction and time-frequency feature extraction, specifically including:

[0059] Frequency domain feature extraction: Apply the fast Fourier transform to each time series data to convert it into frequency domain features:

[0060] ;

[0061] : The original time series data;

[0062] : Fourier transform;

[0063] : Frequency domain data;

[0064] Time-frequency feature extraction: Apply wavelet transform to each time series data to extract time-frequency features:

[0065] ;

[0066] : Original time series data;

[0067] : Wavelet transform;

[0068] : Time-frequency data;

[0069] Step 300, construct the first time series data and the second time series data by combining the obtained frequency domain data and time-frequency data with the original time series data;

[0070] In an embodiment of the present invention, the constructed time series data form is:

[0071] ;

[0072] : Original time series data;

[0073] : Frequency domain data;

[0074] : Time-frequency data;

[0075] : Constructed time series data;

[0076] Step 400, construct a fault detection model, and input the constructed first time series data and second time series data into the fault detection model to output a fault detection result;

[0077] Step 401, take the constructed first time series data and second time series data as inputs and output the first hidden state and the second hidden state;

[0078] In an embodiment of the present invention, apply CNN and LSTM to execute step 401, and the specific formula is as follows:

[0079] ;

[0080] : Constructed time series data;

[0081] CNN: Convolution function;

[0082] : Convolution output feature;

[0083] ;

[0084] ;

[0085] ;

[0086] ;

[0087] ;

[0088] ;

[0089] : Convolution output feature;

[0090] 、 、 : Input gate, forget gate, output gate;

[0091] 、 、 、 : First, second, third, fourth weight matrices;

[0092] 、 、 、 : First, second, third, fourth input matrices;

[0093] 、 、 、 : First, second, third, fourth bias terms;

[0094] : Hidden state at the t-th time step;

[0095] : Cell state;

[0096] : Hyperbolic tangent function;

[0097] : Sigmoid function.

[0098] In an embodiment of the present invention, both the first time-series data and the second time-series data are used to obtain the first hidden state and the second hidden state through CNN and LSTM.

[0099] Step 402, fuse the first hidden state and the second hidden state to output a fused hidden state;

[0100] In one embodiment of the present invention, the calculation formula for hidden state fusion is as follows:

[0101] ;

[0102] : The first hidden state at the -th time step;

[0103] : The second hidden state at the -th time step;

[0104] : Concatenation function;

[0105] : Fused hidden state;

[0106] Step 403, map the fused hidden state to the output through the first fully connected layer to obtain the fault detection result;

[0107] In one embodiment of the present invention, the calculation formula for the first fully connected layer is as follows:

[0108] ;

[0109] : First output weight coefficient;

[0110] : Fused hidden state;

[0111] : First output bias term;

[0112] : Sigmoid function;

[0113] : The output is the fault detection result.

[0114] In one embodiment of the present invention, the output fault detection result is a vector, and the -th component of the vector represents the probability value of the -th fault. If the probability value is higher than 0.5, it indicates that the fault has occurred; otherwise, it indicates normal.

[0115] In an embodiment of the present invention, the occurring faults include: bearing faults: wear, damage, lack of lubrication; impeller faults: cracks, wear, cavitation; shaft seal faults: seal failure, leakage; cavitation faults: too low suction pressure; motor faults: overload, winding damage; electrical control system faults: control element failure; abnormal flow: mismatch between inlet and outlet flow rates; liquid level control problems: abnormal liquid level measurement; abnormal water quality: abnormal pH value, COD value, sand content; system overload faults: excessive pressure, excessive current; operation and setting problems: start-up delay, abnormal liquid level control; lubrication faults: lack of lubrication, contamination.

[0116] In an embodiment of the present invention, the second hidden state is connected to the second fully connected layer for separate pre-training, and the calculation formula of the second fully connected layer is as follows:

[0117] ;

[0118] : The second output weight coefficient;

[0119] : The th second hidden state at the time step;

[0120] : The second output bias term;

[0121] : The sigmoid function;

[0122] : The output is the fault state of the shaft seal.

[0123] In an embodiment of the present invention, the fault states of the shaft seal include: normal, slight, moderate, severe.

[0124] The fault detection model needs to update its parameters through training. In an embodiment of the present invention, it is trained by the method of supervised training, and the multi-class cross-entropy loss function is used as the loss function of the model. The designed loss function is as follows:

[0125] ;

[0126] : The number of samples in the training set for each iteration.

[0127] : The number of fault categories;

[0128] : The sample in the category with the true label;

[0129] : The prediction probability of the model for the sample belonging to the category ;

[0130] : The logarithm of the prediction probability, used to calculate the difference between the prediction and the true label;

[0131] : The average cross-entropy loss value of the entire batch of samples.

[0132] The embodiments of the present invention have been described above, but these embodiments are not limited to the above specific implementation manners. The above specific implementation manners are merely illustrative and not restrictive. Under the inspiration of these embodiments, those of ordinary skill in the art can also make more equivalent embodiments in various forms, all of which fall within the protection scope of these embodiments.

Claims

1. A drainage facility fault diagnosis method in a water networking environment, characterized in that: The following steps are involved: Step 100, installing sensors to obtain drainage facility information and shaft seal monitoring information for n consecutive time steps before the current time step, wherein the drainage facility information includes: electrical system information, mechanical system information, hydraulic system information, liquid level control system information, and water quality related information; Step 200, performing feature engineering on the drainage facility information and shaft seal monitoring information data acquired at n time steps to obtain frequency domain data and time-frequency data; Step 300, constructing first time series data and second time series data according to the obtained frequency domain data and time-frequency data combined with the original time series data; Step 400, constructing a fault detection model, and inputting the constructed first time series data and second time series data into the fault detection model, and outputting a fault detection result; The specific execution process of step 400 is as follows: Step 401, outputting a first hidden state and a second hidden state according to the constructed first time series data and the second time series data as input; Step 402, performing hidden state fusion on the first hidden state and the second hidden state, and outputting a fused hidden state; Step 403, mapping the fused hidden state to the output through the first fully connected layer to obtain a fault detection result.

2. The method for diagnosing drainage facility faults in a water network environment according to claim 1, characterized in that: Electrical system information includes: motor voltage, motor current, insulation resistance value, control element action time; Mechanical system information includes: horizontal, vertical and axial vibration acceleration, vibration spectrum, temperature, noise decibel value; Hydraulic system information includes: water inlet flow, water outlet flow, water inlet pressure value, water outlet pressure value; Liquid level control system information includes: liquid level measurement value, liquid level change rate; Water quality related information includes: pH value, sediment content, chemical oxygen demand COD; The shaft seal monitoring information includes: shaft seal temperature, seal chamber pressure, and shaft seal vibration frequency.

3. The method for diagnosing drainage facility faults in a water network environment according to claim 1, characterized in that: The frequency of data collection is fixed, and the default value is to collect data once every 1 minute.

4. The method for diagnosing drainage facility faults in a water network environment according to claim 1, characterized in that: The first time series data and the second time series data are both derived from the first hidden state and the second hidden state through CNN and LSTM.

5. The method for diagnosing drainage facility faults in a water network environment according to claim 1, characterized in that: The output fault detection result is a vector. The component represents the The probability value of a fault. If the probability value is higher than 0.5, it means the fault has occurred. If it does not exist, it means it is normal.

6. The method for diagnosing drainage facility faults in a water network environment according to claim 1, characterized in that: The second hidden state is connected to the second fully connected layer for separate pre-training, and the output of the second fully connected layer is the fault state of the shaft seal; The fault conditions of the shaft seal include: normal, slight, moderate, and severe.

7. The method for diagnosing drainage facility faults in a water network environment according to claim 1, characterized in that: The fault detection model needs to update parameters through training. It is trained through supervised training and uses the multi-classification cross entropy loss function as the loss function of the model. The designed loss function is as follows: ； : The number of samples in the training set for each iteration; : The number of fault categories; :sample In category The real label on :Model for samples Belongs to category The predicted probability of : The logarithm of the predicted probability, used to calculate the difference between the predicted and true labels; : The average cross entropy loss value of the entire batch of samples.

8. The method for diagnosing drainage facility faults in a water network environment according to claim 1, characterized in that: Feature engineering includes frequency domain feature extraction and time-frequency feature extraction.

9. A drainage facility fault diagnosis method in a water network environment according to claim 8, characterized in that: Frequency domain feature extraction: Apply fast Fourier transform to each time series data to convert it into frequency domain features: ； : original time series data; : Fourier transform; : frequency domain data; Time-frequency feature extraction: Apply wavelet transform to each time series data to extract time-frequency features: ； : original time series data; : Wavelet transform; : Time-frequency data.

10. A drainage facility fault diagnosis system in a water networking environment, characterized in that: It is used to execute a drainage facility fault diagnosis method in a water networking environment as described in any one of claims 1-9.

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