Fanrang Kong

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.

A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis

The research in fault diagnosis for rolling element bearings has been attracting great interest in recent years. This is because bearings are frequently failed and the consequence could cause unexpected breakdown of machines. When a fault is occurring in a bearing, periodic impulses can be revealed in its generated vibration frequency spectrum. Different types of bearing faults will lead to impulses appearing at different periodic intervals. In order to extract the periodic impulses effectively, numerous techniques have been developed to reveal bearing fault characteristic frequencies. In this study, an adaptive varying-scale morphological analysis in time domain is proposed. This analysis can be applied to one-dimensional signal by defining different lengths of the structure elements based on the local peaks of the impulses. The analysis has been first validated by simulated impulses, and then by real bearing vibration signals embedded with faulty impulses caused by an inner race defect and an outer race defect. The results indicate that by using the proposed adaptive varying-scale morphological analysis, the cause of bearing defect could be accurately identified even the faulty impulses were partially covered by noise. Moreover, compared to other existing methods, the analysis can be functioned as an efficient faulty features extractor and performed in a very fast manner.

Machine fault signature analysis by midpoint-based empirical mode decomposition

The fault signature can be revealed by vibration analysis in machine fault detection and diagnosis. Empirical mode decomposition (EMD) is a self-adaptive method that can decompose a vibration signal into informative intrinsic mode functions (IMFs). This paper addresses the improvement of the weakness of the traditional EMD algorithm and presents a new midpoint-based EMD method for effective fault signature analysis of a rotating machine. In the proposed method, geometrical midpoints of successive extrema are employed to estimate the local mean of an analyzed signal. Signal decomposition is then self-adaptively performed to achieve IMFs through removal of the midpoint-based local means. The representative IMF containing fault information is selected for identifying the fault signature. The effectiveness of the proposed method was verified by means of simulation and an application to gear fault diagnosis. Results indicated that the midpoint-based EMD is valuable in improving fault signature analysis of the rotating machine in comparison with the traditional EMD method.

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.

Adaptive Multiscale Noise Tuning Stochastic Resonance for Health Diagnosis of Rolling Element Bearings

The analysis of vibration or acoustic signals is most widely used in the health diagnosis of rolling element bearings. One of the main challenges for vibration or acoustic bearing diagnosis is that the weak signature of incipient defects is generally swamped by severe surrounding noise in the acquired signals. This problem can be solved by the stochastic resonance (SR) approach, which is to enhance the desired signal by the aid of noise. This paper presents an adaptive multiscale noise tuning SR (AMSTSR) for effective and efficient fault identification of rolling element bearings. A new criterion, called weighted power spectrum kurtosis (WPSK), is proposed as the optimization index without prior knowledge of the bearing fault condition. The WPSK concerns both the kurtosis in signal power spectrum and the similarity to a sinusoidal signal in signal waveform, thus it can balance the enhancement of possible characteristic frequency in the frequency domain and the regularity of the signal in the time domain for the SR performance. Two parameters in the AMSTSR, including the cutoff wavelet decomposition level and the tuning parameter, are simultaneously optimized based on the WPSK index through the artificial fish swarm algorithm. The AMSTSR is further applied to the health diagnosis of rolling element bearings and four experimental case studies verify the effectiveness of the proposed method in adaptive identification of the bearing characteristic frequencies.

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

A Doppler Transient Model Based on the Laplace Wavelet and Spectrum Correlation Assessment for Locomotive Bearing Fault Diagnosis

The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully.

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.

Facial feature point extraction using a new improved Active Shape Model

In this paper, we present a new improved Active Shape Model (ASM) for facial feature points extraction. ASM performance is often influenced by some factors such as initial position, illumination, pose, etc, which frequently lead to the local minima in optimization. The original ASM has two sub-models: global shape model and local texture model, we proposed two improved methods for the shortcomings. First, we use a new method to obtain the pupil center position accurately, and these centers can provide more accurate initial position for the point distribution model of ASM. Second, we establish two texture submodels, and they constitute the new texture model together with the original: one based on YCrCb space and color similarity, and the other based on the non-skin region texture feature in the normal direction of facial feature point. The new method of pupil center location can make the average shape transform into initial shape and most similar to the actual feature points model; the new texture model can locate feature points precisely, it can make points located in the contour well. Our experiments of the proposed method have shown effectiveness comparing with the conventional.

Stochastic Resonance in an Underdamped System with Pinning Potential for Weak Signal Detection

Stochastic resonance (SR) has been proved to be an effective approach for weak sensor signal detection. This study presents a new weak signal detection method based on a SR in an underdamped system, which consists of a pinning potential model. The model was firstly discovered from magnetic domain wall (DW) in ferromagnetic strips. We analyze the principle of the proposed underdamped pinning SR (UPSR) system, the detailed numerical simulation and system performance. We also propose the strategy of selecting the proper damping factor and other system parameters to match a weak signal, input noise and to generate the highest output signal-to-noise ratio (SNR). Finally, we have verified its effectiveness with both simulated and experimental input signals. Results indicate that the UPSR performs better in weak signal detection than the conventional SR (CSR) with merits of higher output SNR, better anti-noise and frequency response capability. Besides, the system can be designed accurately and efficiently owing to the sensibility of parameters and potential diversity. The features also weaken the limitation of small parameters on SR system.