Qingbo He

Effects of multiscale noise tuning on stochastic resonance for weak signal detection

Noise enhanced signal detection via stochastic resonance (SR) is generally realized by white noise tuning with an optimal noise intensity. This paper explores a new mechanism of SR that is induced by the noise at multiple scales for enhanced detection of weak signals under heavy background noise. A strategy is proposed to realize the SR via multiscale noise tuning according to the property of 1/f noise. The presented new method combines the benefits of colored noise and parameter tuning to the SR phenomenon. Under the strategy, effects of noise intensity, analysis scale, and driving frequency on the SR are analyzed through numerical simulations. Three merits are displayed for the proposed multiscale noise-induced SR model: insensitivity to noise intensity, activity of multiple scale noise, and capability of detecting high frequency. A practical application to structural defect identification has confirmed the effectiveness of the proposed method in comparison with traditional methods.

Time-Frequency Manifold as a Signature for Machine Health Diagnosis

Time-frequency analysis can reveal an intrinsic signature for representing nonstationary signals for machine health diagnosis. This paper proposes a novel time-frequency signature, called time-frequency manifold (TFM), by addressing manifold learning on generated time-frequency distributions (TFDs). The TFM is produced in three steps. First, the phase space reconstruction (PSR) is employed to reconstruct the inherent dynamic manifold embedded in an analyzed signal. Second, the TFDs are calculated to represent the nonstationary information in the phase space. Third, manifold learning is conducted on the TFDs to discover the intrinsic time-frequency structure of the manifold. The TFM combines nonstationary information and nonlinear information and may thus provide a better representation of machine health pattern. By evaluating the characteristics of top two TFMs, a synthetic TFM signature is further proposed to improve the time-frequency structure. The effectiveness of the TFM signature is verified by means of simulation studies and applications to diagnosis of gear fault and bearing defects. Results indicate the excellent merits of the new signature in noise suppression and resolution enhancement for machine fault signature analysis and health diagnosis.

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.

Structure damage localization with ultrasonic guided waves based on a time-frequency method

The ultrasonic guided wave is widely used for structure health monitoring with the sparse piezoelectric actuator/transducer array in recent decades. It is based on the principle that the damage in the structure would reflect or scatter the wave pulse and thus, the damage-scattered signal could be applied as the feature signal to distinguish the damage. Precise measurement of time of the flight (TOF) of the propagating signal plays a pivotal role in structure damage localization. In this paper, a time-frequency analysis method, Wigner-Ville Distribution (WVD), is applied to calculate the TOF of signal based on its excellent time-frequency energy distribution property. The true energy distribution in the time-frequency domain is beneficial to reliably locate the position of damage. Experimental studies are demonstrated for damage localization of one-dimensional and two-dimensional structures. In comparison with traditional Hilbert envelope and Gabor wavelet transform methods, the proposed WVD-based method has better performance on the accuracy and the stability of damage localization in one-dimensional structure. In addition, the proposed scheme is validated to work effectively for damage imaging of a two-dimensional structure.

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.