Incipient mechanical fault detection based on multifractal and MTS methods

An incipient mechanical fault detection method, combining multifractal theory and Mahalanobis-Taguchi system （MTS）, which is based on statistical technology, is proposed in this paper. Multifractal features of vibration signals obtained from machine state monitoring are extracted by multifractal spectrum analysis and generalized fractal dimensions. Considering the situation of mass samples of normal mechanical running state and few fault states, the feature parameters corresponding to different mechanical running states are further optimized by a statistical method, based on which incipient faults are subsequently identified and diagnosed accurately. Experimental results proved that the method combining multifractal theory and MTS can be used for incipient fault state recognition effectively during the mechanical running process, and the accuracy of fault state identification is improved.

Incipient Gear Fault Detection Using Adaptive Impulsive Wavelet Filter Based on Spectral Negentropy

Adaptive wavelet filtering is a very important fault feature extraction method in the domain of condition monitoring;however, owing to the time-consuming computation and difficulty of choosing criteria used to represent incipient faults, the engineering applications are limited to some extent. To detect incipient gear faults at a fast speed, a new criterion is proposed to optimize the parameters of the modified impulsive wavelet for constructing an optimal wavelet filter to detect impulsive gear faults. First, a new criterion based on spectral negentropy is proposed. Then, a novel search strategy is applied to optimize the parameters of the impulsive wavelet based on the new criterion. Finally,envelope spectral analysis is applied to determine the incipient fault characteristic frequency. Both the simulation and experimental validation demonstrated the superiority of the proposed approach.

Adaptive multiscale wavelet-guided periodic sparse representation for bearing incipient fault feature extraction

Currently, accurately extracting early-stage bearing incipient fault features is urgent and challenging. This paper introduces a novel method called adaptive multiscale wavelet-guided periodic sparse representation(AMWPSR) to address this issue. For the first time, the dual-tree complex wavelet transform is applied to construct the linear transformation for the AMWPSR model.This transform offers superior shift invariance and minimizes spectrum aliasing. By integrating this linear transformation with the generalized minimax concave penalty term, a new sparse representation model is developed to recover faulty impulse components from heavily disturbed vibration signals. During each iteration of the AMWPSR process, the impulse periods of sparse signals are adaptively estimated, and the periodicity of the latest sparse signal is augmented using the final estimated period. Simulation studies demonstrate that AMWPSR can effectively estimate periodic impulses even in noisy environments, demonstrating greater accuracy and robustness in recovering faulty impulse components than existing techniques.Further validation through research on two sets of bearing life cycle data shows that AMWPSR delivers superior fault diagnosis results.

Distributed Fault-Tolerant Consensus Tracking of Multi-Agent Systems Under Cyber-Attacks

This paper investigates the distributed fault-tolerant consensus tracking problem of nonlinear multi-agent systems with general incipient and abrupt time-varying actuator faults under cyber-attacks.First,a decentralized unknown input observer is established to estimate relative states and actuator faults.Second,the estimated and output neighboring information is combined with distributed fault-tolerant consensus tracking controllers.Criteria of reaching leader-following exponential consensus tracking of multi-agent systems under both connectivity-maintained and connectivity-mixed attacks are derived with average dwelling time,attack frequency,and attack activation rate technique,respectively.Simulation example verifies the effectiveness of the fault-tolerant consensus tracking algorithm.

A Synthesized Design of Adaptive and Sliding-Mode Observer-Based Incipient Fault Detection for a Quadrotor

This study in westigatn the fault detection and fault atimation problem of a quadrotar with disturbanea.A synthesiand design of adaptive and sliding mode obeerver is propoeed to addres the efkctive detection and atimation of inepient faulta.First,the decom pased subaystems are obtalned through the coardinate transdormation,and the in Stial and ineipkent faults are sea rated from the disturbanon.Second,an adaptive obeerver is applied to the decamposd un petubad subaystem to atimate ineipient faults,while the sliding mode obearver remalns robust to disturbanos for the perturbed subaytem.Lyapumov stahility theory mmas the mavergenae o dynamic erors and the stability of the quadrotor ayatem.Pinally,the dfc tiveess of the proposed synthated algod thm of ineipient fault detection is weified by the quadrotor simulation.