Siliang Lu

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

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.

Effects of underdamped step-varying second-order stochastic resonance for weak signal detection

Abstract Stochastic resonance (SR) has been proved to be an effective approach for weak signal detection. In this paper, an underdamped step-varying second-order SR (USSSR) method is proposed to further improve the output signal-to-noise ratio (SNR). In the method, by selecting a proper underdamped damping factor and a proper calculation step, the weak periodic signal, the noise and the potential can be matched with each other in the regime of second-order SR to generate an optimal dynamical system. The proposed method has three distinct merits as: 1) secondary filtering effect produces a low-noise output waveform; 2) good band-pass filtering effect attenuates the multiscale noise that locates in high- and (or) low-frequency domains; and 3) good anti-noise capability in detecting weak signal being submerged in heavy background noise. Numerical analysis and application verification are performed to confirm the effectiveness and efficiency of the proposed method in comparison with a traditional SR method.

Enhanced Rotating Machine Fault Diagnosis Based on Time-Delayed Feedback Stochastic Resonance

The fault-induced impulses with uneven amplitudes and durations are always accompa-nied with amplitude modulation and (or) frequency modulation, which leads to that theacquired vibration/acoustic signals for rotating machine fault diagnosis always presentnonlinear and nonstationary properties. Such an effect affects precise fault detection,especially when the impulses are submerged in heavy background noise. To address thisissue, a nonstationary weak signal detection strategy is proposed based on a time-delayed feedback stochastic resonance (TFSR) model. The TFSR is a long-memorysystem that can utilize historical information to enhance the signal periodicity in the feed-back process, and such an effect is beneficial to periodic signal detection. By selectingthe proper parameters including time delay, feedback intensity, and calculation step inthe regime of TFSR, the weak signal, the noise, and the potential can be matched witheach other to an extreme, and consequently a regular output waveform with low-noiseinterference can be obtained with the assistant of the distinct band-pass filtering effect.Simulation study and experimental verification are performed to evaluate the effective-ness and superiority of the proposed TFSR method in comparison with a traditional sto-chastic resonance (SR) method. The proposed method is suitable for detecting signalswith strong nonlinear and nonstationary properties and (or) being subjected to heavymultiscale noise interference. [DOI: 10.1115/1.4030346]Keywords: rotating machine fault diagnosis, weak signal detection, stochastic resonance,time-delayed feedback

Note: Signal amplification and filtering with a tristable stochastic resonance cantilever

This Note reports a tristable cantilever that exploits stochastic resonance (SR) phenomenon for a study of signal amplification and filtering. The tristable device system combines the benefits of bistable system (wide interwell spacing) and monostable system (smooth motion in potential). The prototype tristable cantilever exhibits 42 times root-mean-square amplitude, 35.86 dB power gain, advance of 15 dB signal-to-noise ratio, and twice fidelity at around 7.6 Hz as compared to the input signal. In a wide operating bandwidth [5.5 Hz, 8.2 Hz], the tristable SR cantilever outperforms the traditional monostable cantilever and bistable SR cantilever in these characteristics.

Periodic fault signal enhancement in rotating machine vibrations via stochastic resonance

This paper proposes a novel approach to periodic fault signal enhancement in rotating machine vibrations with a tristable mechanical vibration amplifier (TMVA) by exploiting stochastic resonance (SR). The TMVA is a nonlinear physical structure system that consists of a cantilever beam and a magnet system. Through the TMVA, the periodic weak signal can be amplified with the assistance of noise in the regime of SR. Benefitting from a wider interwell spacing and a smoother potential curve, the TMVA produces a more regular output waveform with lower noise in a wider operating bandwidth as compared to the monostable and bistable amplifiers. Different from the traditional signal enhancement approach which is based on digital signal processing (DSP) techniques, the designed physical structure can realize signal enhancement in a simple, intuitive, effective and adaptive way without too much complex operations. The effectiveness and efficiency of the proposed approach are validated by a simulated fault signal and the practical bearing and gearbox fault signals, in comparison with a traditional DSP-based SR method. The principle of the proposed approach shows potential applications on rotating machine fault diagnosis area and other areas related to weak periodic signal enhancement.

Stochastic Resonance with a Joint Woods-Saxon and Gaussian Potential for Bearing Fault Diagnosis

This work aims for a new stochastic resonance (SR) model which performs well in bearing fault diagnosis. Different from the traditional bistable SR system, we realize the SR based on the joint of Woods-Saxon potential (WSP) and Gaussian potential (GP) instead of a reflection-symmetric quartic potential. With this potential model, all the parameters in the Woods-Saxon and Gaussian SR (WSGSR) system are not coupled when compared to the traditional one, so the output signal-to-noise ratio (SNR) can be optimized much more easily by tuning the system parameters. Besides, a smoother potential bottom and steeper potential wall lead to a stable particle motion within each potential well and avoid the unexpected noise. Different from the SR with only WSP which is a monostable system, we improve it into a bistable one as a general form offering a higher SNR and a wider bandwidth. Finally, the proposed model is verified to be outstanding in weak signal detection for bearing fault diagnosis and the strategy offers us a more effective and feasible diagnosis conclusion.

Online Fault Diagnosis of Motor Bearing via Stochastic-Resonance-Based Adaptive Filter in an Embedded System

Digital signal processing algorithms are widely adopted in motor bearing fault diagnosis. However, most algorithms are developed on desktop platforms, and their focus is on the analysis of offline captured signals. In this paper, a simple and easily implemented algorithm running on an embedded system is proposed for the online fault diagnosis of motor bearing. The core part of the algorithm is a stochastic-resonance-based adaptive filter that realizes signal denoising and adaptation of the filter coefficient. Processed by the filter, the period of the purified signal is obtained, and then the fault type of the motor bearing is identified. The proposed method has distinct merits, such as low computational cost, online implementation, contactless measurement, and availability for various speed motors. This paper provides a simple, flexible, and effective solution for conducting motor bearing diagnosis on an embedded/portable device. The algorithm proposed is validated by a brushless dc motor and a brushed dc motor fabricating with defective/healthy support bearings.

Remaining Useful Life Prediction of Rolling Bearings Using PSR, JADE, and Extreme Learning Machine

Rolling bearings play a pivotal role in rotating machinery. The degradation assessment and remaining useful life (RUL) prediction of bearings are critical to condition-based maintenance. However, sensitive feature extraction still remains a formidable challenge. In this paper, a novel feature extraction method is introduced to obtain the sensitive features through phase space reconstitution (PSR) and joint with approximate diagonalization of Eigen-matrices (JADE). Firstly, the original features are extracted from bearing vibration signals in time and frequency domain. Secondly, the PSR is applied to embed the original features into high dimensional phase space. The between-class and within-class scatter () are calculated to evaluate the feature sensitivity through the phase point distribution of different degradation stages and then different weights are assigned to the corresponding features based on the calculated . Thirdly, the JADE is employed to fuse the weighted features to obtain the advanced features which can better reflect the bearing degradation process. Finally, the advanced features are input into the extreme learning machine (ELM) to train the RUL prediction model. A set of experimental case studies are carried out to verify the effectiveness of the proposed method. The results show that the extracted advanced features can better reflect the degradation process compared to traditional features and could effectively predict the RUL of bearing.

Note: On-line weak signal detection via adaptive stochastic resonance.

We design an instrument with a novel embedded adaptive stochastic resonance (SR) algorithm that consists of a SR module and a digital zero crossing detection module for on-line weak signal detection in digital signal processing applications. The two modules are responsible for noise filtering and adaptive parameter configuration, respectively. The on-line weak signal detection can be stably achieved in seconds. The prototype instrument exhibits an advance of 20 dB averaged signal-to-noise ratio and 5 times averaged adjust R-square as compared to the input noisy signal, in considering different driving frequencies and noise levels.

A Novel Contactless Angular Resampling Method for Motor Bearing Fault Diagnosis Under Variable Speed

This paper proposes a novel contactless angular resampling method for motor bearing fault diagnosis under variable speed. This method involves three steps: (1) the instantaneous rotating angle is measured using the Kanade-Lucas-Tomasi object tracking algorithm from a video that is recorded by a high-speed camera; (2) the bearing signal that is acquired using a microphone is resampled in the angular domain based on the accumulated angle curve; and (3) the resampled signal is demodulated to obtain the bearing fault characteristic frequency for order analysis (OA) and fault recognition. This method has been proven effective in diagnosing a set of defective bearings installed on a brushless direct current motors and a direct current motors test rigs, respectively. As its main contribution, this paper exploits a novel optical measurement method to estimate rotating speed, thereby avoiding the use of a tachometer and overcoming the limitations of conventional tacholess OA methods.