CN117213856A - Bearing residual life prediction method based on Yun Bian double-data-source fusion - Google Patents

Abstract

The invention discloses a bearing remaining life prediction method based on the fusion of cloud-edge dual data sources. It belongs to the field of equipment health management and maintenance and is implemented by a bearing remaining life prediction system based on the fusion of cloud-edge dual data sources. The method includes the following steps: Step 1: Perform wavelet denoising on historical bearing degradation data; Step 2: Extract and analyze the time domain and frequency domain degradation features of the denoised signal; Step 3: Screen the horizontal and vertical degradation features. Construct horizontal and vertical training sets; Step 4: Normalization operation; Step 5: Training the Transformer model; Step 6: Vibration signals collected in real time; Step 7: Construct a degradation feature test set; Step 8: Weighting based on double errors ‑DS evidence is fused to obtain the remaining life prediction value of the bearing. The present invention can effectively solve the problem that the accuracy of the existing bearing remaining service life prediction is not high and does not take into account the real-time prediction, and ensures the real-time status monitoring and life prediction of the bearing.

Description

Bearing remaining life prediction method based on cloud-edge dual data source fusion

Technical field

The present invention is a method for predicting the remaining life of bearings based on the fusion of cloud-edge dual data sources. It belongs to the field of equipment health management and maintenance and is particularly suitable for prediction of the remaining life of bearings based on the fusion of cloud-edge dual data sources.

Background technique

With the rapid development of science and technology and the continuous innovation of engineering technology, more and more rotating machinery and equipment are currently functioning in daily life and production. As an important part of rotating machinery, bearings are widely used in many fields such as aerospace and rail transportation. If the bearings show signs of degradation or damage without real-time condition monitoring and processing, more serious equipment damage will often occur. In addition, with the vigorous development of industrial Internet of Things technology, the data generated by rotating machinery is particularly prominent, and the data used for bearing condition monitoring accounts for 40%. Uploading such a huge amount of data to the cloud server in real time will not only put the network under huge traffic pressure, but also make it difficult for the server to process the massive data to ensure the timeliness of bearing status monitoring. Then, how to monitor the health status and predict the remaining useful life (RUL) of important bearings in rotating machinery in real time has become an urgent problem in the research field.

The traditional bearing RUL method mainly predicts from the perspective of failure mechanism and statistical probability. Both methods have the disadvantages of difficult modeling and lack of training data, so there is great difficulty in bearing RUL. With the development of artificial intelligence technology, new information technology methods based on data-driven are widely used in bearing RUL. This method achieves accurate prediction of the remaining life of the bearing by extracting eigenvalues that characterize the degradation state of the bearing as covariates for prediction. There are currently two main forms of data-driven bearing RUL methods: ① only use the original signal as input, and use models such as temporal convolutional networks and deep belief networks to mine deep feature learning degradation patterns; ② extract the degradation trend from the original signal. Features are then input into the deep learning model for prediction.

The above method ① requires a large amount of data for training and adjustment, and it is difficult to fully extract degradation features from the original data. For the method of extracting degradation features from the original signal and then carrying out RUL, Recursive Neural Network (RNN) is applied to RUL with its significant time series signal processing capabilities. However, when processing time series data, the serial operation mode of RNN seriously reduces the computing speed, and RNN's ability to capture long time series dependencies is weak, and it is easy to cause gradient disappearance and gradient explosion. The Transformer model realizes parallel computing and long-distance feature capture that RNN cannot achieve through its position coding and multi-head self-attention mechanism, which improves prediction accuracy while reducing computing time.

To sum up, in view of the problem that the current prediction accuracy of the remaining service life of rolling bearings is not high and the real-time prediction is not taken into account, how to ensure real-time condition monitoring and life prediction of bearings is the focus and difficulty of research in this field.

Contents of the invention

The purpose of this invention is to provide a bearing remaining life prediction method based on Cloud-Edge Collaborative Computing (CECC) dual data source fusion (DSF), aiming to utilize the real-time nature of cloud and edge computing linkage, The reliability of the two-dimensional sensor and the accuracy of the Transformer model can be used to improve the prediction accuracy while reducing the calculation time, solving the problem that the existing bearing remaining service life prediction accuracy is not high and the real-time prediction is not taken into account.

In order to achieve the above objects, the present invention provides the following technical solutions:

The bearing remaining life prediction method based on the fusion of cloud and edge dual data sources realizes its function by the bearing remaining life prediction system based on the fusion of cloud and edge dual data sources. The bearing remaining life prediction system based on cloud-edge dual data source fusion includes a cloud server, a horizontal edge computing device, a vertical edge computing device, a horizontal vibration sensor, and a vertical vibration sensor. It is characterized in that the horizontal vibration sensor and vertical vibration sensors are respectively installed in the horizontal and vertical directions of the bearing to detect the vibration of the bearing; the horizontal edge calculation device and the vertical edge calculation device are respectively connected to the horizontal vibration sensor and the vertical vibration sensor. The vibration signals uploaded by the horizontal vibration sensor and the vertical vibration sensor are obtained in real time and features are extracted; the horizontal edge computing device and the vertical edge computing device are equipped with network communication modules, which can be connected to the cloud server network.

The horizontal edge computing device and the vertical edge computing device are computers based on CPU or FPGA and have memory.

The bearing remaining life prediction method based on the fusion of cloud-edge dual data sources is characterized by including the following steps:

Step 1: The cloud server performs wavelet denoising on the bearing historical degradation data;

Step 2: The cloud server extracts time domain and frequency domain degradation features from the denoised signal and conducts feature analysis and selection;

Step 3: The cloud server constructs the filtered horizontal and vertical degradation features into horizontal and vertical training sets respectively;

Step 4: The cloud server normalizes the horizontal and vertical training sets and labels;

Step 5: The cloud server uses the training set and labels to train the horizontal Transformer model and the vertical Transformer model respectively;

Step 6: The vibration signals collected in real time by the horizontal vibration sensor and the vertical vibration sensor are transmitted to the horizontal edge computing device and the vertical edge computing device respectively;

Step 7: The horizontal edge computing device and the vertical edge computing device denoise and extract the degradation features of the horizontal and vertical vibration signals respectively, build a degradation feature test set and upload it to the cloud server in real time;

Step 8: The cloud server inputs the horizontal and vertical degradation feature test sets into the trained horizontal and vertical Transformer models respectively to predict the remaining life of the horizontal and vertical signal bearings, and fuses them based on the double error weighted-DS evidence to obtain the remaining life of the bearing. Life expectancy.

Furthermore, the described steps 1 to 5 can be processed in an offline manner; the described steps 6 to 8 are processed online, and parallel computing can be used to improve real-time performance.

Furthermore, the wavelet denoising process described in step 1 is to use the wavelet threshold denoising method to denoise the historical degraded data, specifically: perform a 5-layer denoising process on the original noisy signal with the Dobesi fourth-order wavelet as the mother wavelet. Discrete wavelet decomposition performs threshold processing and wavelet reconstruction on detail coefficients to remove noise and retain information that can express degradation characteristics.

Furthermore, the degradation feature extraction and selection described in step 2 is specifically: extract 27 feature parameters in the time domain and frequency domain from the denoised signal, and then know based on expert prior knowledge that good degradation features should have good Characteristics such as monotonicity, trend, and robustness are analyzed. The characteristics are analyzed for monotonicity, trend, and robustness. Suitable degradation characteristic parameters are selected, including standard deviation, variance, peak-to-peak value, root mean square, maximum value, and absolute value. Ten degradation characteristics include average value, waveform factor, margin factor, frequency root mean square, and frequency mean.

Furthermore, the normalization of the training set described in step 4 adopts maximum and minimum value normalization; the label is normalized to the ratio of the remaining service life to the full life, which satisfies a linear function relationship with the bearing operating time.

Furthermore, the horizontal Transformer model and the vertical Transformer model are composed of position encoding, multi-head attention mechanism and other modules. The mean square error is used as the loss function, the Adam optimizer is selected to train and optimize the model, and the learning rate is set to 0.0001.

Furthermore, the fusion of double error weighted-DS evidence described in step eight is specifically:

(1) Calculate the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the horizontal signal remaining life prediction results and the vertical signal remaining life prediction results respectively;

(2) Based on the root mean square error, calculate the initial weight of the root mean square error Among them, Rmse _x is the root mean square error of the horizontal signal remaining life prediction result, and Rmse _y is the root mean square error of the vertical signal remaining life prediction result;

(3) Based on the average absolute error, calculate the initial weight of the root mean square error Determined based on the average absolute error, where Mae _x is the average absolute error of the horizontal signal remaining life prediction result, Mae _y is the average absolute error of the vertical signal remaining life prediction result;

(4) Use double error weighted-DS evidence to calculate horizontal and vertical fusion weights

( ₅ ) Fusion _of evidence to obtain _{the predicted} value _of the remaining life of _the bearing pred _{i =} pred Remaining life prediction value.

Beneficial effects of the present invention: The present invention proposes a bearing remaining life prediction method based on the fusion of cloud-edge dual data sources. This method combines the high real-time performance of edge computing, the efficient data processing capability of artificial intelligence technology, and the high reliability of multi-sensor technology. Realizing the accuracy and timeliness of RUL prediction can effectively solve the problem of low accuracy of existing bearing remaining service life prediction and failure to consider the real-time prediction, and ensure real-time status monitoring and life prediction of bearings.

Description of drawings

In order to make the purpose, technical solutions and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration:

Figure 1 is a framework diagram of the bearing remaining life prediction system based on cloud-edge dual data source fusion provided by the present invention;

Figure 2 is a flow chart of the bearing remaining life prediction method based on cloud-side dual data source fusion according to the present invention;

Figure 3 is the prediction result of LSTM in Embodiment 1 of the present invention;

Figure 4 shows the prediction effect of the bearing remaining life prediction method based on cloud-edge dual data source fusion in Embodiment 1 of the present invention.

Detailed ways

The following describes the experimental implementation of the present invention through specific examples. Those skilled in the art can easily understand the advantages of the present invention based on the content of this description. The present invention can also be implemented through other different specific cases, and various details in this specification can also be modified or changed in various ways based on different viewpoints without departing from the spirit of the present invention.

Example 1: The IEEE Reliability Society and the FEMTO-ST Institute organized the IEEE PHM 2012 Data Challenge. The challenge focuses on estimating the remaining service life of bearings, which is a critical issue since most rotating machine failures are related to these components, greatly affecting the availability, safety and cost-effectiveness of machinery, systems and equipment in industries such as power and transportation. .

For the PHM2012 data set, the present invention provides a "bearing remaining life prediction method based on cloud-edge dual data source fusion", which is implemented by a bearing remaining life prediction system based on cloud-edge dual data source fusion. Combined with Figure 1, the described The bearing remaining life prediction system based on the fusion of cloud-edge dual data sources includes: cloud server (1), horizontal edge computing device (2), vertical edge computing device (3), horizontal vibration sensor (4), vertical vibration sensor ( 5), characterized in that the horizontal vibration sensor (4) and vertical vibration sensor (5) are composed of two micro accelerometers positioned at 90° to each other, and are respectively placed radially between the vertical axis of the bearing outer ring and On the horizontal axis, the signal sampling frequency is set to 25.6kHz, the sampling interval is 10s, and each sampling time is 0.1s, that is, 2560 vibration data are collected in one sampling. The horizontal edge computing device (2) and the vertical edge computing device (3) are connected to the horizontal vibration sensor (4) and the vertical vibration sensor (5) respectively, and are used to obtain the horizontal vibration sensor (4) and the vertical vibration in real time. The vibration signal uploaded by the sensor (5) is used to extract features; the horizontal edge computing device (2) and the vertical edge computing device (3) are equipped with network communication modules, which can be connected to the cloud server (1). The horizontal edge computing device (2) and the vertical edge computing device (3) use AMD Ryzen 5 3600 6-Core Processor (main frequency 3.6GHz) 64-bit operating system, with a memory of 8GB; the cloud server ( 1) It is a supercomputer, Inter Xeon Gold 6330 28Core (clocked at 2.0GHz), 56 threads, and 6TB of memory.

This embodiment is implemented based on the Tensorflow deep learning framework.

Combined with Figure 2, the method includes the following steps:

S1: The cloud server (1) performs wavelet denoising on historical bearing degradation data;

S2: The cloud server (1) extracts time domain and frequency domain degradation features of the denoised signal and conducts feature analysis and selection;

S3: The cloud server (1) constructs the filtered horizontal and vertical degradation features into horizontal and vertical training sets respectively;

S4: The cloud server (1) normalizes the horizontal and vertical training sets and labels;

S5: The cloud server (1) uses the training set and labels to train the horizontal Transformer model and the vertical Transformer model respectively;

S6: The vibration signals collected in real time by the horizontal vibration sensor (4) and the vertical vibration sensor (5) are transmitted to the horizontal edge computing device (2) and the vertical edge computing device (3) respectively;

S7: The horizontal edge computing device (2) and the vertical edge computing device (3) perform denoising and extraction of degradation features on the horizontal and vertical vibration signals respectively, build a degradation feature test set and upload it to the cloud server in real time (1);

S8: The cloud server (1) inputs the horizontal and vertical degradation feature test sets into the trained horizontal and vertical Transformer models respectively to predict the remaining life of the horizontal and vertical signal bearings, and fuses them based on the double error weighted-DS evidence to obtain the bearing remaining life prediction value.

In step S2:

The denoised signal is denoised on the fourth set of life-long degradation data set with a radial force of 4000N and a rotation speed of 1800r/min. The data set has a total of 1428 samples, and each sample has a horizontal vibration signal and a vertical vibration signal. Moreover, both the horizontal acceleration vibration signal and the vertical acceleration vibration signal contain effective information characterizing the degradation characteristics.

In step S3:

According to the prior expert knowledge, the bearing failure point is judged to be the 1085th group, and 60% of the data from the fault occurrence to complete degradation period is used as the training set of the model, that is, the 1085th to 1287th group of data is used as the training set for the model. Conduct training.

In step S4:

The actual remaining life is used as the label y for training and testing. The label is set from 0 to 1. Label 1 represents that the bearing is intact and not used, and label 0 represents that the bearing has completely failed. This data set has a total of 1428 sets of data. When the sample is the 1300th set of data, the label of the remaining life of the bearing is 1-1300/1428=0.0896. By analogy, the label of the remaining life of the rolling bearing is constructed.

In step S5:

Set the Transformer model time step to 1, use the Adam optimizer to optimize the loss in the training process during the experiment, and set the learning rate to 0.0001.

In step S8:

The fusion of double error weighted-DS evidence is specifically:

(1) Calculate the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the horizontal signal remaining life prediction results and the vertical signal remaining life prediction results respectively;

(2) Based on the root mean square error, calculate the initial weight of the root mean square error Among them, Rmse _x is the root mean square error of the horizontal signal remaining life prediction result, and Rmse _y is the root mean square error of the vertical signal remaining life prediction result;

(3) Based on the average absolute error, calculate the initial weight of the root mean square error Determined based on the average absolute error, where Mae _x is the average absolute error of the horizontal signal remaining life prediction result, Mae _y is the average absolute error of the vertical signal remaining life prediction result;

(4) Use double error weighted-DS evidence to calculate horizontal and vertical fusion weights

( ₅ ) Fusion _of evidence to obtain _{the predicted} value _of the remaining life of _the bearing pred _{i =} pred Remaining life prediction value.

Table 1 Performance comparison of Example 1 of the present invention

In order to better demonstrate the advantages of the method of the present invention, the LSTM model of a single-dimensional signal is now used to predict the remaining life of the bearing in the PHM2012 data set as a control group. The prediction results are shown in Figure 3. At the same time, based on the cloud-side dual data sources The prediction results of the fused bearing remaining life prediction method are shown in Figure 4. Furthermore, the results shown in Table 1 were obtained by comparing the root mean square error, mean absolute error and calculation time.

It can be seen that the bearing remaining life prediction method proposed by the present invention based on the fusion of cloud-edge dual data sources is far ahead in terms of accuracy and efficiency.

Embodiment 2: In order to better demonstrate the advantages of dual data source fusion, on the basis of Embodiment 1, the data of a single horizontal signal, a single vertical signal and dual data sources are processed respectively. The invention provides a "based on "Bearing remaining life prediction method based on cloud-edge dual data source fusion" is implemented by a bearing remaining life prediction system based on cloud-edge dual data source fusion.

It is particularly emphasized that the steps and system description in this embodiment are the same as those in Embodiment 1, and will not be described further here.

The difference is that in step S3:

In the experiment, the data set was divided into a training set and a test set. Based on the prior expert knowledge, the bearing fault occurrence point was judged to be the 1085th group, and 60% of the data from the fault occurrence to complete degradation period was used as the training set of the model, that is, the data The 1085 to 1287 sets of data are used as training sets to train the model; the remaining 40% of the data, that is, 141 sets of data, are used as the test set of the cloud-edge collaborative computing model for prediction.

In step S4:

The actual remaining life is used as the label y for training and testing. The label is set from 0 to 1. Label 1 represents that the bearing is intact and not used, and label 0 represents that the bearing has completely failed.

In step S8:

The prediction results are output based on the data of a single horizontal signal, a single vertical signal and dual data sources respectively, wherein the fusion of the dual data sources is that the horizontal and vertical weights are both 0.5 for fusion, and the double error weighted-DS evidence of the present invention fusion. The results obtained are shown in Table 2. It can be seen that the prediction results of dual data sources are better than the results of single data, and the fusion of double error weighted-DS evidence is better than the fixed weight fusion.

Table 2 Comparison of fusion experiment results in Example 2

The above embodiments only illustrate the principles and effects of the present invention, but are not intended to limit the present invention. Anyone familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the invention. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed in the present invention shall still be covered by the claims of the present invention.

Claims (7)

1. The method for predicting the residual life of the bearing based on Yun Bian double-data-source fusion is realized by a Yun Bian double-data-source fusion-based bearing residual life prediction system, and the cloud edge double-data-source fusion-based bearing residual life prediction system comprises the following steps: cloud server (1), horizontal direction edge computing device (2), vertical direction edge computing device (3), horizontal vibration sensor (4), vertical vibration sensor (5), its characterized in that: the horizontal vibration sensor (4) and the vertical vibration sensor (5) are respectively arranged in the horizontal direction and the vertical direction of the bearing and are used for detecting the vibration condition of the bearing; the horizontal direction edge computing device (2) and the vertical direction edge computing device (3) are respectively connected with the horizontal vibration sensor (4) and the vertical vibration sensor (5) and are used for acquiring vibration signals uploaded by the horizontal vibration sensor (4) and the vertical vibration sensor (5) in real time and extracting characteristics; the horizontal edge computing device (2) and the vertical edge computing device (3) are provided with network communication modules, and can be connected with a cloud server (1) in a network manner; the method comprises the following steps:

step one: the cloud server (1) performs wavelet denoising processing on the bearing history degradation data;

step two: the cloud server (1) extracts time domain and frequency domain degradation characteristics of the denoising signals and performs characteristic analysis and selection;

step three: the cloud server (1) respectively constructs the screened horizontal and vertical degradation characteristics into a horizontal training set and a vertical training set;

step four: the cloud server (1) normalizes the horizontal training set, the vertical training set and the labels;

step five: the cloud server (1) respectively trains the horizontal transducer model and the vertical transducer model by utilizing the training set and the labels;

step six: vibration signals acquired in real time by the horizontal vibration sensor (4) and the vertical vibration sensor (5) are respectively transmitted to the horizontal edge computing device (2) and the vertical edge computing device (3);

step seven: the horizontal direction edge computing device (2) and the vertical direction edge computing device (3) respectively conduct denoising processing and degradation characteristic extraction on the horizontal vibration signal and the vertical vibration signal, construct a degradation characteristic test set and upload the degradation characteristic test set to the cloud server (1) in real time;

step eight: the cloud server (1) respectively inputs the horizontal degradation characteristic test set and the vertical degradation characteristic test set into a trained horizontal transducer model and a trained vertical transducer model to predict the residual life of the horizontal signal bearing and the residual life of the vertical signal bearing, and fuses the horizontal signal bearing and the vertical signal bearing according to double-error weighting-DS evidence to obtain a residual life prediction value of the bearing.

2. The method for predicting the residual life of the bearing based on cloud edge double data source fusion according to claim 1, wherein the method comprises the following steps of: the first to fifth steps are processed in an off-line mode; and the sixth to eighth steps are on-line processing modes, and real-time performance is improved by parallel calculation.

3. The method for predicting the residual life of the bearing based on cloud edge double data source fusion according to claim 1, wherein the method comprises the following steps of: the step one of wavelet denoising is to denoise historical degradation data by adopting a wavelet threshold denoising method, and specifically comprises the following steps: and 5 layers of discrete wavelet decomposition taking a plurality of Bei Xisi-order wavelets as mother wavelets is carried out on the original noise-containing signal, and the detail coefficients are subjected to threshold processing and wavelet reconstruction so as to remove noise and retain information capable of representing degradation characteristics.

4. The method for predicting the residual life of the bearing based on cloud edge double data source fusion according to claim 1, wherein the method comprises the following steps of: the degradation characteristic extraction and selection specifically comprises the following steps: 27 characteristic parameters of a time domain and a frequency domain are extracted from the denoised signal, then the characteristics of good monotonicity, trending and robustness of good degradation characteristics are obtained according to expert priori knowledge, the characteristics are subjected to monotonicity, trending and robustness analysis, and the proper degradation characteristic parameters are selected to comprise ten degradation characteristics of standard deviation, variance, peak-to-peak value, root mean square, maximum value, absolute average value, waveform factor, margin factor, frequency root mean square and frequency average value.

5. The method for predicting the residual life of the bearing based on cloud edge double data source fusion according to claim 1, wherein the method comprises the following steps of: the training set normalization adopts maximum and minimum normalization; the label normalizes to be the ratio of remaining life to full life, satisfies the one-time function relation with bearing operating time.

6. The method for predicting the residual life of the bearing based on cloud edge double data source fusion according to claim 1, wherein the method comprises the following steps of: the horizontal transducer model and the vertical transducer model use mean square error as a loss function, and an Adam optimizer is selected to train and optimize the models.

7. The method for predicting the residual life of the bearing based on cloud edge double data source fusion according to claim 1, wherein the method comprises the following steps of: the merging of the double-error weighted-DS evidence in the step eight is specifically as follows:

(1) Calculating a root mean square error (Root Mean Square Error, RMSE) and an average absolute error (Mean Absolute Error, MAE) of the horizontal signal remaining life prediction result and the vertical signal remaining life prediction result, respectively;

(2) Based on the root mean square error, calculating the initial weight of the root mean square error Wherein Rmse _x Rmse, root mean square error, as a result of the residual life prediction of the horizontal signal _y Root mean square error as a result of the vertical signal residual life prediction;

(3) Based on the average absolute error, calculating the initial weight of the root mean square error Based on the average absolute error, mae of _x Average absolute error of residual life prediction result for horizontal signal Mae _y Average absolute error of the vertical signal residual life prediction result;

(4) Calculating horizontal and vertical fusion weights using dual error weighted-DS evidence

(5) Obtaining the residual life prediction value pred of the bearing by fusing evidence _i ＝pred _x ·Rul _xi +pred _y ·Rul _yi The method comprises the steps of carrying out a first treatment on the surface of the Therein Rul _xi 、Rul _yi Pred, the prediction of the remaining life of the horizontal and vertical signals, respectively _i Is a residual life prediction value of the bearing.