Journal ArticleDOI
Applications of fault diagnosis in rotating machinery by using time series analysis with neural network
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TLDR
The difference values of AR coefficients indicated that the AR coefficients of ideal signal for normal machine are deducted from faulty machines, which indicated the new fault diagnosis method by using the difference of AR coefficient with BPNN was proposed.Abstract:
The common diagnosis method of time series analysis is an autoregressive (AR) method, which is a kind of math model that can be established by time difference and vibration amplitude. As the AR model utilized the math method for fitting the variable, the AR coefficients represent the signal features and can be used to determine fault types. This study proposed the difference values of AR coefficients, which indicated that the AR coefficients of ideal signal for normal machine are deducted from faulty machines. It is convention that the relationship between the difference values of AR coefficients and fault types as trained by using back-propagation neural network (BPNN). The new fault diagnosis method by using the difference of AR coefficients with BPNN was proposed in this study. The diagnosis results were obtained and compared with the three methods, which include the difference of AR coefficients with BPNN, the AR coefficients with BPNN and the distance of AR coefficients method for 23 samples. And the diagnosis results obtained by using the difference of AR coefficients with BPNN were superior to AR coefficients with BPNN and distance of AR coefficients methods.read more
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References
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Journal ArticleDOI
Neural-network-based motor rolling bearing fault diagnosis
TL;DR: Simulation and real-world testing results obtained indicate that neural networks can be effective agents in the diagnosis of various motor bearing faults through the measurement and interpretation of motor bearing vibration signatures.
Journal ArticleDOI
Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter
H. Endo,Robert B. Randall +1 more
TL;DR: In this paper, the authors proposed the use of the minimum entropy deconvolution (MED) technique to enhance the ability of the existing autoregressive (AR) model based filtering technique to detect localised faults in gears.
Journal ArticleDOI
Neural networks in process fault diagnosis
TL;DR: A multilayer perceptron network with a hyperbolic tangent as the nonlinear element seems best suited for the task of fault diagnosis in a realistic heat exchanger-continuous stirred tank reactor system.
Journal ArticleDOI
Autoregressive Model-Based Gear Fault Diagnosis
Wenyi Wang,Albert Kai-Sun Wong +1 more
TL;DR: In this article, a model-based technique for the detection and diagnosis of gear faults was proposed based on the signal averaging technique, the proposed technique first establishes an autoregressive (AR) model on the vibration signal of the gear of interest in its healthy state.
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