A systematic second-order output spectrum based method for fault diagnosis with a local tuning approach

2020 ◽  
Vol 475 ◽  
pp. 115283
Author(s):  
Quankun Li ◽  
Xingjian Jing
Keyword(s):  
Fault Diagnosis ◽  
Second Order ◽  
Output Spectrum ◽  
Local Tuning

A second-order output spectrum based method for detecting bolt-loosening fault in a satellite-like structure

2019 ◽  
Vol 26 (4) ◽  
pp. 157-165
Author(s):  
Quankun Li ◽  
Xingjian Jing
Keyword(s):  
Second Order ◽  
Bolted Joint ◽  
Output Spectrum ◽  
Engineering Structures ◽  
Local Tuning ◽  
Damage Indicator ◽  
Non Linear ◽  
Linear Behaviour ◽  
Novel Method ◽  
Bolt Loosening

Bolt-loosening faults frequently exist in industrial engineering structures since these bolted structures are often subjected to vibrations or the like in their service process. In this paper, a novel method based on the second-order output spectrum (SOOS) is proposed to detect potential bolt-loosening faults in a complex satellite-like structure. In this method, a general multi-degree-of-freedom (MDOF) model simulating bolt-loosening faults induced non-linearities and inherent boundary or material non-linearities by non-linear forces is built to describe the non-linear behaviour of the structure, and then a local damage indicator is derived for bolt-loosening fault detection through a local tuning approach (LTA) which tunes local structural properties. Results of experimental cases demonstrate that the state of bolted joint in the satellite-like structure with inherent non-linearities can be estimated by this novel SOOS based method effectively and reliably.

scholarly journals A New Second-Order Tristable Stochastic Resonance Method for Fault Diagnosis

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 965 ◽  
Author(s):  
Lu Lu ◽  
Yu Yuan ◽  
Heng Wang ◽  
Xing Zhao ◽  
Jianjie Zheng
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Fitness Function ◽  
Resonance Method ◽  
Poor Performance ◽  
Rolling Bearing ◽  
Second Order ◽  
Weak Signals ◽  
Rolling Bearings ◽  
Sr Method

Vibration signals are used to diagnosis faults of the rolling bearing which is symmetric structure. Stochastic resonance (SR) has been widely applied in weak signal feature extraction in recent years. It can utilize noise and enhance weak signals. However, the traditional SR method has poor performance, and it is difficult to determine parameters of SR. Therefore, a new second-order tristable SR method (STSR) based on a new potential combining the classical bistable potential with Woods-Saxon potential is proposed in this paper. Firstly, the envelope signal of rolling bearings is the input signal of STSR. Then, the output of signal-to-noise ratio (SNR) is used as the fitness function of the Seeker Optimization Algorithm (SOA) in order to optimize the parameters of SR. Finally, the optimal parameters are used to set the STSR system in order to enhance and extract weak signals of rolling bearings. Simulated and experimental signals are used to demonstrate the effectiveness of STSR. The diagnosis results show that the proposed STSR method can obtain higher output SNR and better filtering performance than the traditional SR methods. It provides a new idea for fault diagnosis of rotating machinery.

The second-order output spectrum-based method for fault localization in ring type structures

2019 ◽  
Vol 98 (3) ◽  
pp. 1935-1955 ◽  
Author(s):  
Quankun Li ◽  
Xingjian Jing ◽  
Yingqing Guo
Keyword(s):  
Fault Localization ◽  
Second Order ◽  
Output Spectrum ◽  
Ring Type

scholarly journals Multisensor Fused Fault Diagnosis for Rotation Machinery Based on Supervised Second-Order Tensor Locality Preserving Projection and Weighted k-Nearest Neighbor Classifier under Assembled Matrix Distance Metric

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Fen Wei ◽  
Gang Wang ◽  
Bingyin Ren ◽  
Jianghua Ge ◽  
Yaping Wang
Keyword(s):  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Second Order ◽  
Distance Metric ◽  
Order Tensor ◽  
Locality Preserving Projection ◽  
Nearest Neighbor Classifier ◽  
Locality Preserving ◽  
Diagnosis Method ◽  
Neighbor Classifier

In order to sufficiently capture the useful fault-related information available in the multiple vibration sensors used in rotation machinery, while concurrently avoiding the introduction of the limitation of dimensionality, a new fault diagnosis method for rotation machinery based on supervised second-order tensor locality preserving projection (SSTLPP) and weighted k-nearest neighbor classifier (WKNNC) with an assembled matrix distance metric (AMDM) is presented. Second-order tensor representation of multisensor fused conditional features is employed to replace the prevailing vector description of features from a single sensor. Then, an SSTLPP algorithm under AMDM (SSTLPP-AMDM) is presented to realize dimensional reduction of original high-dimensional feature tensor. Compared with classical second-order tensor locality preserving projection (STLPP), the SSTLPP-AMDM algorithm not only considers both local neighbor information and class label information but also replaces the existing Frobenius distance measure with AMDM for construction of the similarity weighting matrix. Finally, the obtained low-dimensional feature tensor is input into WKNNC with AMDM to implement the fault diagnosis of the rotation machinery. A fault diagnosis experiment is performed for a gearbox which demonstrates that the second-order tensor formed multisensor fused fault data has good results for multisensor fusion fault diagnosis and the formulated fault diagnosis method can effectively improve diagnostic accuracy.

scholarly journals Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

2021 ◽  
Vol 11 (23) ◽  
pp. 11480
Author(s):  
Hongjiang Cui ◽  
Ying Guan ◽  
Wu Deng
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Second Order ◽  
Weak Signal ◽  
Ant Colony Optimization Algorithm ◽  
Resonance Theory ◽  
Mode Decomposition ◽  
Diagnosis Method

Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.

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