A Method of Fault Diagnosis for Rotary Equipment Based on Deep Learning

Cheng, Zhang; Xu, Lei; Li, Xingwang; Wang, Huiyun

doi:10.1109/phm-chongqing.2018.00171

Cited by 42 publications

(16 citation statements)

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“…As shown in the flowchart of CFT algorithm in Fig.3, first, according to original feature data of the UAV, we can get the sampling time t and sampling value f (x). Second, we determine that the subinterval [x n , x n+1 ] is [1], [2] and the interpolation order M is equal to 11. According to the relevant formulas in Section II, we can also calculate other necessary parameters and intermediate variables.…”

Section: A Cft Algorithm Processing Layermentioning

confidence: 99%

“…In the traditional fault diagnosis strategy, the diagnosis effect directly depends on the characteristic information of the system input data [2]. With the development of the digital information age, the data of much complex mechanical equipment have gradually become massive and intelligent, which makes it difficult to meet the analysis needs for traditional feature extraction methods.…”

Section: Introductionmentioning

confidence: 99%

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Fusion Fault Diagnosis Model for Six-Rotor UAVs Based on Conformal Fourier Transform and Improved Self-Organizing Feature Map

Che

2021

IEEE Access

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If the fault sources can be located in a timely and accurate manner when an unmanned aerial vehicle (UAV) has an early minor fault, a major accident can be avoided. In light of the early fault of the six-rotor UAV motors, this paper proposes a fusion fault diagnosis model based on Conformal Fourier Transform (CFT) and the improved Self-Organizing Feature Map (SOM) neural network. Firstly, to overcome the weakening of UAV fault information caused by external interference and solve the problem that the non-uniform distribution of the collected flight data affects the accuracy of the frequency domain processing, on the basis of changing the processing method of the Fourier integral kernel and using high-order numerical integration, an improved Conformal Fourier Transform (CFT) algorithm optimized by spectrum refinement strategy is proposed. Then this algorithm is used to further intensify the main features and build a clear fault state in a cycle processing method. Secondly, an improved SOM neural network is designed. We use the unweighted average distance (UPGMA) of the agglomerative hierarchical clustering algorithm to detect the clustering areas of each pattern category of the data samples, and the weight vectors of different clustering areas are used to randomly initialize the connection weights of SOM network, thereby improving the clustering speed and convergence effect. On this basis, CFT algorithm is integrated into the improved SOM network to realize the multi-module collaboration strategy and finally form a new fusion fault diagnosis model. INDEX TERMS CFT algorithm, improved SOM network, main feature intensification, hierarchical clustering, fault diagnosis.

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Section: A Cft Algorithm Processing Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fusion Fault Diagnosis Model for Six-Rotor UAVs Based on Conformal Fourier Transform and Improved Self-Organizing Feature Map

Che

2021

IEEE Access

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“…The frameworks for fault diagnosis with the traditional method and the DBN based method are given in Fig. 2 according to [1], [41]. In the traditional procedure, feature extraction is used to extract the fault related features from data collected by multi-sensors based on signal processing techniques.…”

Section: Figure 1 Architecture Of Deep Belief Networkmentioning

confidence: 99%

A Deep Learning Based Fault Diagnosis Method With Hyperparameter Optimization by Using Parallel Computing

Guo

et al. 2020

IEEE Access

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Bearing fault diagnosis is of great significance to ensure the safe operation of mechanical equipment. This paper proposes an intelligent fault diagnosis method of rolling bearings based on deep belief network (DBN) with hyperparameter optimization by using parallel computing. Different with traditional diagnosis methods that extract the features manually depending on much prior knowledge about signal processing techniques and diagnostic expertise, DBN extracts fault features automatically by machine learning mechanism. Considering the time consuming problem, parallel computing is adopted to the DBN training process by using a Master/Slave mode to improve the training speed so that the global optimization with Genetic Algorithm and higher diagnosis accuracy can be achieved. Finally, the proposed method is verified with the public datasets provided by Case Western Reserve University (CWRU) with various fault depths in different locations and loads of rolling bearings. The results indicate that the proposed method can identify bearing faults under different conditions correctly which significantly enhances the intelligence of fault classification and reduces the time for parameter selection of deep learning models. INDEX TERMS Deep belief network, hyperparameter optimization, parallel computing, fault diagnosis I. INTRODUCTION

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“…Artificial neural networks (ANN) are employed using supervised training and can detect faults after learning hidden relations in the data [17]; however, they lack visual representation and can suffer from overfitting. Use of Deep Learning (DL) techniques have become increasingly popular in the latter years due to an increase in power computation, higher availability of information, and high accuracy, many DL models have been tested such as Dense Neural Networks [18], Deep Belief Networks [19][20][21], Convolutional Neural Networks [22,23], Recurrent Neural Networks [24,25], and Generative Neural Networks [26], however, in most of the examples the DL model is adapted only to the plant in the study, therefore a change of plant will require a change of the methodology. Some works have also been devoted to the elaboration of hybrid techniques, modelbased plus data-based systems for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis via Neural Ordinary Differential Equations

2021

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Implementation of model-based fault diagnosis systems can be a difficult task due to the complex dynamics of most systems, an appealing alternative to avoiding modeling is to use machine learning-based techniques for which the implementation is more affordable nowadays. However, the latter approach often requires extensive data processing. In this paper, a hybrid approach using recent developments in neural ordinary differential equations is proposed. This approach enables us to combine a natural deep learning technique with an estimated model of the system, making the training simpler and more efficient. For evaluation of this methodology, a nonlinear benchmark system is used by simulation of faults in actuators, sensors, and process. Simulation results show that the proposed methodology requires less processing for the training in comparison with conventional machine learning approaches since the data-set is directly taken from the measurements and inputs. Furthermore, since the model used in the essay is only a structural approximation of the plant; no advanced modeling is required. This approach can also alleviate some pitfalls of training data-series, such as complicated data augmentation methodologies and the necessity for big amounts of data.

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A Method of Fault Diagnosis for Rotary Equipment Based on Deep Learning

Cited by 42 publications

References 1 publication

Fusion Fault Diagnosis Model for Six-Rotor UAVs Based on Conformal Fourier Transform and Improved Self-Organizing Feature Map

Fusion Fault Diagnosis Model for Six-Rotor UAVs Based on Conformal Fourier Transform and Improved Self-Organizing Feature Map

A Deep Learning Based Fault Diagnosis Method With Hyperparameter Optimization by Using Parallel Computing

Fault Diagnosis via Neural Ordinary Differential Equations

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