Research on Fault Diagnosis Based on Singular Value Decomposition and Fuzzy Neural Network

Abstract

A method based on singular value decomposition (SVD) and fuzzy neural network (FNN) was proposed to extract and diagnose the fault features of diesel engine crankshaft bearings efficiently and accurately. Firstly, vibration signals of crankshaft bearings in known state under the same working condition were decomposed by EMD to obtain the modal components containing faultfeature information. Then, the singular values of modal components which include the main fault features were used as the initial vector matrix, where the eigenvectors were decomposed to form a fault characteristic matrix. At last, the fault features matrix was trained by the fuzzy neural network, in order to realize the diagnosis and identification of the crankshaft bearings in different states in the form of numerical values. The experiment showed that the numerical identification of the fuzzy neural network based on the singular value had high fault diagnosis accuracy and stability. This method can also reflect the gradual change of the crankshaft bearings' fault to some extent, so it has the desired reliability and value.

A Bearing Performance Degradation Modeling Method Based on EMD‐SVD and Fuzzy Neural Network

“…Each IMF's frequency and composition are different and the decomposition process is self-adaptive, so EMD is very suitable for dealing with nonstationary bearing vibration signals. In the process of EMD, each IMF component must satisfy the two following conditions: (1) the number of the extreme points and the zero points should be equal or not more than one; (2) the mean of the maximum and minimum of extreme points is zero at any point in the signal curve [16,18,19]. So, for the signal X(t), its equation of EMD can be expressed as…”

“…erefore, singular values can reflect the features of eigenvectors very well [16]. So, SVD based on EMD can make up for the deficiency of traditional EMD by the means of matrix operation, which makes the fault-feature extraction more stable in the vibration signals.…”

“…FNN is an important intelligent information processing method, which combines the advantages of fuzzy logic and neural network well. erefore, the FNN not only has strong self-learning ability to deal with data directly but also has strong ability of structural knowledge expression, so it can realize arbitrary nonlinear mapping with arbitrary precision easily [16]. Figure 2 shows the general structure of FNN.…”

“…e singular value changes little when the elements change slightly. erefore, SVD based on traditional EMD can solve this problem effectively [16]. As an important intelligent information processing method, FNN has strong self-learning and direct data processing ability, so it is more suitable for structural knowledge expression, which can realize arbitrary nonlinear mapping with arbitrary precision [17].…”

A Neutrosophic Set Based Fault Diagnosis Method Based on Multi-Stage Fault Template Data

“…Fault diagnosis aims to identify and repair faults in systems, products, and processes, and has been widely applied to various fields, for instance, military [1,2], economic [3,4], and medicine [5,6], and plays a significant part in the prevention of accidents during the normal operation of equipment [7,8]. Owing to the complexity and uncertainty of the actual environment, fault information is usually imprecise, incomplete, and uncertain, and it is thus, difficult to cope with [9][10][11][12].…”