Stochastic resonance in a single-well potential and its application in rolling bearing fault diagnosis

High-Performance Adaptive Weak Fault Diagnosis Based on the Global Parameter Optimization Model of a Cascaded Stochastic Resonance System

Stochastic resonance (SR), as a type of noise-assisted signal processing method, has been widely applied in weak signal detection and mechanical weak fault diagnosis. In order to further improve the weak signal detection performance of SR-based approaches and realize high-performance weak fault diagnosis, a global parameter optimization (GPO) model of a cascaded SR system is proposed in this work. The cascaded SR systems, which involve multiple multi-parameter-adjusting SR systems with both bistable and tri-stable potential functions, are first introduced. The fixed-parameter optimization (FPO) model and the GPO models of the cascaded systems to achieve optimal SR outputs are proposed based on the particle swarm optimization (PSO) algorithm. Simulated results show that the GPO model is capable of achieving a better SR output compared to the FPO model with rather good robustness and stability in detecting low signal-to-noise ratio (SNR) weak signals, and the tri-stable cascaded SR system has a better weak signal detection performance compared to the bistable cascaded SR system. Furthermore, the weak fault diagnosis approach based on the GPO model of the tri-stable cascaded system is proposed, and two rolling bearing weak fault diagnosis experiments are performed, thus verifying the effectiveness of the proposed approach in high-performance adaptive weak fault diagnosis.

Nonstationary feature extraction based on stochastic resonance and its application in rolling bearing fault diagnosis under strong noise background

When the load and speed of rotating machinery change, the vibration signal of rolling bearing presents an obvious nonstationary characteristic. Stochastic resonance (SR) mainly is convenient to analyze the stationary feature of vibration signals with high signal-to-noise ratio. However, it is difficult for SR to extract the nonstationary feature of rolling bearings under strong noise background. For one thing, the frequency change of nonstationary signals makes the occurrence of SR very difficult. For another, the features of rolling bearings are large parameters and further prevent the SR method from performing well. Therefore, combined with order analysis (OA), adaptive frequency-shift SR is presented in this paper. To solve the problem of frequency change, OA is used to convert the nonstationary feature into stationary feature, which resamples the nonstationary signal in the time domain to stationary signal in the angular domain. To solve the other problem, the frequency-shift method based on Fourier transform is adopted to move the fault feature frequency to low frequency, and thus SR is more likely to occur under small parameter conditions. The simulated and experimental results indicate that not only the amplitude of fault feature but also the signal-to-noise ratio is significantly improved. These demonstrate that the fault features of rolling bearing in variable speed conditions are extracted successfully.

A field programmable gate array based high speed real-time weak periodic signal detection technique

The detection of a high repetition rate weak signal is studied in this paper. Owing to the characteristics of the signal of interest, both high speed analog-to-digital converter (ADC) and low computational complexity data processing techniques are required for high speed real-time weak signal detection. In this paper, a novel field programmable gate array (FPGA) based high speed real-time periodic weak signal detection technique is presented. Cascaded comparators outside the FPGA and cascaded flip flops in the FPGA are used to implement a one-bit ADC, which performs quantization first followed by sampling. Based on this novel design, a time-interleaved structure with several sub-channels is further proposed to significantly improve the sampling rate of the one-bit ADC, which does not require calibration for offset, gain, and sample-time mismatches between sub-channels. Each sub-channel has a long-time coherent integration structure to coherently integrate the sampled one-bit data. A full layer clearance mechanism that only operates on specific bits of the integrated sums is proposed to overcome the influence of a noise baseline drift on the weak signal detection. Compared with the traditional adaptive threshold, this mechanism has a significantly lower computational complexity. A prototype with three sub-channels performing 1.5 Gs/s sampling is implemented to verify the proposed technique. The results obtained confirm its high sampling rate and noise baseline drift tolerance in weak signal detection.