Sequential Multiscale Noise Tuning Stochastic Resonance for Train Bearing Fault Diagnosis in an Embedded System

Abstract

Multiscale noise tuning stochastic resonance (MSTSR) has been proved to be an effective method for enhanced fault diagnosis by taking advantage of noise to detect the incipient faults of the bearings and gearbox. This paper addresses a sequential algorithm for the MSTSR method to detect the train bearing faults in an embedded system through the acoustic signal analysis. Specifically, the energy operator, digital filter array, and fourth rank Runge-Kutta equation methods are designed to realize the signal demodulation, multiscale noise tuning, and bistable stochastic resonance in sequence. The merit of the sequential algorithm is that it reduces the memory consumption and decreases the computation complexity, so that it can be efficiently implemented in the embedded system based on a low-cost, low-power hardware platform. After the sequential algorithm, the real-valued fast Fourier transform is used to calculate the power spectrum of the analyzed signal. The proposed method has been verified in algorithm performance and hardware implementation by three kinds of practical acoustic signals from defective train bearings. An enhanced performance of the proposed fault diagnosis method is confirmed as compared with several traditional methods, and the hardware performance is also validated.Index Terms-Embedded system, fault diagnosis, multiscale noise tuning, sequential algorithm, stochastic resonance (SR), train bearing.

Wayside Bearing Fault Diagnosis Based on Envelope Analysis Paved with Time-Domain Interpolation Resampling and Weighted-Correlation-Coefficient-Guided Stochastic Resonance

“…Although the Doppler effect has been removed, identifying the FCF from the Doppler-free signal still remains difficult. In the last years, many methods for enhancing the diagnosis-relevant information of rotating machinery, such as digital filters [10,11], wavelet transform [12][13][14], stochastic resonance (SR) [15,16], manifold learning [17,18], and morphological analysis [19,20], have been invented. Among them, SR can utilize noise to enhance the weak periodic signal, which benefits weak signal detection especially when the signal bandwidth is overlapped with the noise bandwidth.…”

Faulty Line Selection Method for Distribution Network Based on Variable Scale Bistable System

“…In recent years, the research on stochastic resonance has made great progress. Stochastic resonance is a new practical technology which uses stochastic resonance principle to detect weak signal, and its research and application have spread into physical fields [18,19], signal processing [20,21], mechanical fault diagnosis [22], biology [23], neural network [24], and other academic fields; however, the research on this technology in power system is still needed. Therefore, with detailed study of the effect of TZSC on bistable system, this paper proposes a novel faulty line selection method for small current to ground system based on stochastic resonance theory.…”

Weak impulsive signals detection based on step-varying asymmetric stochastic resonance

“…Among most of the research, envelope signal is regarded as the input of the SR system which is still asymmetric even DC-cancelled. [13][14][15] With symmetric potential model in conventional bistable SR (CSR, in the application of CSR system, its calculating step is also optimized during the calculation for a more convincible comparison) system, output is generated to be symmetric, which is incongruent with the input one and cannot preserve its original characteristics. Grifoni studied the SR in a temperature range and firstly found the asymmetry in a SR system.…”