TECHNICAL FIELD

The present disclosure belongs to the technical field of fault diagnosis of reciprocating machineries of emergency generating sets for vehicles, ships and nuclear power plants, and in particular relates to a fault diagnosis method of a reciprocating machinery based on a keyphasor-free complete-cycle signal, which can be used to diagnose an abnormal fault of the reciprocating machinery such as improper valve clearance in case a keyphasor or speed transducer can hardly be installed.

BACKGROUND

Reciprocating machinery is the core power unit of emergency generating sets for vehicles, ships and nuclear power plants, and its safe and reliable operation is indispensable for the normal running of the equipment. In order to prevent equipment failures and reduce the impact of failures, traditional regular and post-maintenance requires human experts to enter the site for empirical diagnosis and analysis. At present, a large number of real-time operating condition monitoring and maintenance technologies for the reciprocating machinery have been developed at home and abroad. Due to the excessive dependence of traditional intelligent diagnosis on keyphasor and speed signals, the existing monitoring and diagnosis methods still cannot meet the complex and growing diagnosis requirements of reciprocating machinery. The cylinder head vibration signal of the reciprocating machinery includes the information of key internal moving parts, which can be fully mined to reduce or get rid of restrictions by keyphasor and speed signals. Therefore, the development of a fault diagnosis method based on a keyphasor-free complete-cycle signal is of great significance for diagnosing typical faults of the reciprocating machinery such as improper valve clearance.

At present, there are mainly two methods used to acquire complete-cycle signals. One method is a keyphasor-based method, which includes the acquisition of angular-domain vibration signal based on a micro transducer, the zero-crossing detection of keyphasor signal cycle, the barycentric detection of keyphasor signal cycle and the synchronous complete-cycle resampling of rotor vibration signal by using differential and spline interpolation. The keyphasor-based acquisition method generally has the problem of phase loss. The other method is a keyphasor-free method, which is used for some equipment that is inconvenient to install a keyphasor transducer, including the time-domain synchronous averaging without a keyphasor signal. The keyphasor-free acquisition method is simple and convenient, and avoids the installation of a keyphasor measuring device, thus greatly reducing the cost. Therefore, it has attracted more and more attention. However, at present, both the keyphasor-based and keyphasor-free acquisition methods usually require an auxiliary device, and are complicated, costly, and greatly affected by the load.

SUMMARY

In order to solve the problem of dependence on a keyphasor signal or a complicated and costly auxiliary device, the present disclosure provides a fault diagnosis method of a reciprocating machinery based on a keyphasor-free complete-cycle signal. The present disclosure avoids the use of any external auxiliary device, and has a simple operation and high diagnostic accuracy. The present disclosure provides a cut-off method for acquiring a complete-cycle signal by mining effective information in a vibration signal, which avoids the use of an expensive and difficult-to-install keyphasor transducer. The present disclosure introduces an autoencoder for automatic feature extraction, which avoids manual intervention and achieves high robustness. The present disclosure further uses a support vector machine (SVM) for classification and recognition, and realizes the diagnosis of typical faults of the reciprocating machinery such as improver valve clearance.

To achieve the above purpose, the present disclosure provides a fault diagnosis method of a reciprocating machinery based on a keyphasor-free complete-cycle signal, where the method includes the following steps:

step 1: acquiring, by an online monitoring system of the reciprocating machinery, m groups of vibration acceleration data of a single cycle of a measuring point of a cylinder head in a normal state and an abnormal state respectively on a keyphasor basis to generate a normal data set Nmand an abnormal data set Am; normalizing each group of data in the two data sets by using a z-score method to obtain a processed normal data set N′mand abnormal data set A′m; generating images with a size of h1×h2 from the m groups of data in the normal data set N′mto acquire a normal data image set Pm;

step 2: locating shock positions of an intake stroke, a compression stroke, a power stroke and an exhaust stroke according to keyphasor information of the reciprocating machinery; then, partitioning each image in the normal data image set Pminto sub-images with a size of

h⁢14×h⁢2
according to an order of the intake stroke, the compression stroke, the power stroke and the exhaust stroke; labeling the sub-images to generate an intake stroke image set J={j1, j2, j3, . . . , jm}, a compression stroke image set C={c1, c2, c3, . . . , cm}, a power stroke image set F={f1, f2, f3, . . . , fm} and an exhaust stroke image set E={e1, e2, e3, . . . , em}; dividing the four stroke image sets into a training set and a test set respectively to obtain a training image set Train_C={Jtrain, Ctrain, Ftrain, Etrain} and a test image set Test_C={Jtest, Ctest, Ftest, Etest};

step 3: building and training a two-class hybrid convolutional neural network (CNN) Net;

3.1: representing categories of classification of the network Net by 0 and 1 respectively, where 0 indicates that an input data is not a complete-cycle data, and 1 indicates that the input data is a complete-cycle data; composing the network Net by an intake stroke CNN CNN_J, a compression stroke CNN CNN_C, a power stroke CNN CNN_F and an exhaust stroke CNN CNN_E; adopting a mean cross-entropy loss function Mlossas follows:

Ml⁢o⁢s⁢s=1k⁢∑j=1kLj(qi)⁢Lj(qi)=-[yi·log2(qi)+(1-yi)·log2(1-qi)]

where, qi∈[0,1] represents a predicted value of a one-dimensional vector corresponding to an i-th category; Lj(qi) represents a cross-entropy loss function for category i; k represents a count of CNNs; yirepresents a true value of the i-th category; j represents a j-th CNN;

3.2: setting the four CNNs with the same network structure including an input layer, a convolution layer 1, a pooling layer 1, a convolution layer 2, a pooling layer 2, a fully connected layer 1 and a fully connected layer 2, where an input image in the input layer is a grayscale image of

h⁢14×h⁢2;
output results of the convolution layer 1, the pooling layer 1, the convolution layer 2 and the pooling layer 2 are respectively processed by batch normalization, and output results of the fully connected layer 1 and the fully connected layer 2 are respectively processed by L2 regularization;

3.3: denoting predicted probability values output by CNN_J, CNN_C, CNN_F and CNN_E as gJ, gC, gF, gErespectively, and taking a mean probability value g as a predicted probability value of the two-class hybrid CNN Net, where the mean probability value is calculated as follows:

g=gJ+gC+gF+gE4

inputting the training image set Train_C={Jtrain, Ctrain, Ftrain, Etrain} into a built Net network model for training; testing a trained network model by using the test set Test_C={Jtest, Ctest, Ftest, Etest} to obtain a final two-class hybrid CNN model which is denoted as T_Net; calculating a test accuracy as follows:

Ac⁢c=CorrT⁢e⁢s⁢t-⁢m×1⁢0⁢0⁢%

where, Corr represents a count of correct identification; Test_m represents a total count of test data;

step 4: acquiring a complete-cycle data of the reciprocating machinery on a keyphasor-free basis;

4.1: selecting a measuring point of the cylinder head of the reciprocating machinery, and continuously acquiring normal vibration acceleration data Sn={s1, s2, s3, . . . , sn} and abnormal vibration acceleration data Ab_Sn′={ab_s1, ab_s2, ab_s3, . . . , ab_sn′} of two or more cycles on a keyphasor-free basis, where n and n′ respectively represent a count of sampling points;

4.2: determining, according to a sampling frequency Fsof the monitoring system and a working speed range [Rmin, Rmax] of a unit, a range of sampling point count [Wmin, Wmax] of a complete cycle as follows:

Wmin=1⁢2⁢0Rmax·Fs⁢Wmax=1⁢2⁢0Rmin·Fs

setting a series of window lengths Wv={Wmin, Wmin+d, Wmin+2d, . . . , Wmin+αd}, α=0, 1, 2, . . . , that is, a total of α+1 window lengths, according to [Wmin, Wmax], where d represents a gradient of length change; Wmin+αd≤Wmax, and 10<d≤Wmin;

4.3: performing sliding cut-off on data Snin window Wminwith a step Step to obtain a cut-off result Sn1; traversing data Snthrough the window to obtain a cut-off data set Cut1={Sn11, Sn12, . . . }; normalizing data in Cut1 by the z-score method to obtain a data set Cut1′; generating images with a size of h1×h2 from each group of data in Cut1′ respectively; partitioning each generated image from left to right into sub-images with a size of

h⁢14×h⁢2,
where a partitioning result of each image is denoted as Sn1_P={img1,img2,img3,img4}; calculating the image set of each window in turn to obtain image sets Cut1_P={Sn11_P, Sn12_p, . . . }, Cut2_P={Sn21_p, Sn22_p, . . . }, . . . , Cut(α+1)_P={Sn(α+1)1_p, Sn(α+1)2_p, . . . };

4.4: sequentially selecting and inputting a first part of images in image set Cut1_P into T_Net for initial classification; continuing to classify a remaining image if the selected images include a complete-cycle image, where a data point corresponding to a complete-cycle image recognized by the classification of T_Net is a complete-cycle data, which is denoted as zqi′; stopping classification of the remaining image in Cut1_P and switching to a next window, if the selected images do not include a complete-cycle image; calculating a count of images initially classified as follows:

Count=WinStep

where, Win represents a window in Wv;

4.5: processing the image sets obtained by all windows according to the method in step 4.4 to finally obtain a normal complete-cycle data set T_zq={zq1, zq2, zq3, . . . , zqi′}, i′=1, 2, 3, . . . through keyphasor-free cut-off;

performing cut-off on abnormal data Ab_Sn′by steps 4.2 to 4.5 to obtain cut-off cycle data ab_zqi′, and finally obtaining an abnormal complete-cycle data set Ab_zq={ab_zq1, ab_zq2, ab_zq3, . . . , ab_zqi′,} through keyphasor-free cut-off;

step 5: building an automatic feature extraction model, that is, an autoencoder model; dividing the normal data Nmand abnormal data Amof the vibration acceleration of the cylinder head acquired on a keyphasor basis into a training set Train and a test set Test, and labeling “normal” and “abnormal” respectively; building an autoencoder including an input layer, a hidden layer 1, a hidden layer 2, a hidden layer 3, a hidden layer 4 and an output layer; training an initial autoencoder model by the training set Train, and testing the model by the test set Test to obtain an autoencoder-based feature extraction model; and

step 6: labeling the complete-cycle data sets T_zq and Ab_zq obtained in step 4 as “normal” and “abnormal”, and then dividing the complete-cycle data sets into a training set and a test set respectively according to the same ratio; inputting the training set into the autoencoder model obtained in step 5; taking an intermediate hidden layer feature obtained by the autoencoder as an input into a support vector machine (SVM) model to train a two-class SVM model, which outputs “1” indicating normal or “0” indicating abnormal; classifying normal and abnormal complete-cycle signals of the reciprocating machinery by the two-class SVM model after training.

Compared with the traditional fault diagnosis technology, the present disclosure specifically has the following advantages:

1. According to inherent shock correlation characteristics reflected by cylinder head vibration signals in different strokes of the reciprocating machinery, the present disclosure explores a new way to acquire a complete-cycle signal without the need for a keyphasor device to acquire a keyphasor signal. The present disclosure proposes an adaptive cut-off window strategy to realize multi-scale quasi-cyclic image generation under single or variable working conditions. By fusing the existing advanced deep learning-convolutional neural network (DL-CNN) technology with the working characteristics of the reciprocating machinery, the present disclosure proposes a new deep cascade CNN model for complete-cycle vibration image recognition.

2. The fault diagnosis method proposed by the present disclosure can monitor the complete-cycle vibration signal without using a keyphasor device, and train a deep autoencoder model by using a complete-cycle vibration signal acquired on a keyphasor basis. By utilizing the model to mine the intrinsic features hidden in the vibration signal, the present disclosure achieves more stable, more abstract and more advanced feature extraction without human interference involved in feature design or feature selection.

3. The fault diagnosis method proposed by the present disclosure is convenient and simple, avoids phase lose, and has high cycle integrity and high diagnostic accuracy. It can be applied to diagnose typical faults of the reciprocating machinery (such as improper valve clearance and misfire), and can also be applied to complex units such as gas turbines and compressors.

