Fault Diagnosis of Reciprocating Compressor Based on Convolutional Neural Networks with Multisource Raw Vibration Signals

Abstract

Reciprocating compressors are widely used in petroleum industry. Due to containing complex nonlinear signal, it is difficult to extract the fault features from its vibration signals. This paper proposes a new method named Convolutional Neural Network based on Multisource Raw vibration signals (MSRCNN). The proposed method uses multisource raw vibration signals collected by several sensors as input and uses the designed CNN to operate both the feature extraction and classification. The gas valve signals of reciprocating compressor in different states are used as the experimental data. In order to test the effectiveness of the proposed method, it is compared with the traditional BP (Back-Propagation) neural network fault diagnosis method based on power spectrum energy and wavelet packet energy. In order to further test the antinoise performance of the proposed method, some noisy signals with different signal-to-noise ratios were constructed by adding white noise into sampled signals for testing. The results show that the MSRCNN model has higher fault recognition rate than the traditional methods. This indicates that the MSRCNN method not only has good fault recognition effect, but also has certain antinoise performance.

Compressor Fault Diagnosis Knowledge: A Benchmark Dataset for Knowledge Extraction From Maintenance Log Sheets Based on Sequence Labeling

“…Traditional machine learning approaches apply machine learning theories, such as support vector machine (SVM) [8], artificial neural network (ANN) [9], hidden Markov model (HMM) [10], hybrid method [11] etc. The above research has achieved certain results, but due to the complex structures of compressors, the diagnostic performance of these methods is not ideal [12]. The diagnosis accuracy is a concern since these traditional machine learning methods are not applicable to the increasingly growing data which require high generalization [7].…”

“…In recognizing the drawbacks of the traditional machine learning CFD approaches, researchers have recently explored deep learning CFD approaches to automatically capture, to some extent, useful features from collected raw data, and achieved good performances in compressor fault diagnosis. According to the structures of neural networks used in feature extraction, deep learning CFD approaches can be divided into convolutional neural network (CNN) approaches [12], deep belief network (DBN) approaches [13], stacked auto-encoder (AE) approaches [14], self-attention network, ResNet approaches, etc. Among all these approaches, the CNN and DBN approaches prevail in CFD applications.…”

Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors

“…One of the important merits using the proposed method for the thermal based monitoring is that it needs much less computational effort, compared with the deep learning-based methods [ 31 ]. Moreover, this thermal image analysis-based monitoring approach can have more comprehensive monitoring information at a lower cost of instrumentation and processing effort, compared with vibration [ 3 , 4 , 7 ], acoustics [ 4 , 5 ] and motor current based [ 8 ] monitoring methods.…”

Fault Diagnosis of Reciprocating Compressor Valve Based on Transfer Learning Convolutional Neural Network

“…Some experts have also tried to use convolutional neural networks to solve the problem of fault diagnosis. Yang et al [12] used three sensors to collect vibration signals in case of valve failure of reciprocating compressor. It is directly used as the input of the convolutional neural network and makes full use of its feature of automatic feature extraction.…”