Average Accumulative Based Time Variant Model for Early Diagnosis and Prognosis of Slowly Varying Faults

Zhou, Funa; Park, Ju H.; Wen, Chenglin; Hu, Po

doi:10.3390/s18061804

Cited by 9 publications

(7 citation statements)

References 52 publications

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“…In the rotor-bearing system, the degradation of the component leads to the appearance of the unstable phenomenon [55]. The early degradation detection of component is an essential task for the fault diagnosis and prognosis [56]. The proposed method with the MaxEnt feature-based reliability model is suitable to monitor the incipient degradation of a component by detecting the occurrence of instability.…”

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

MaxEnt feature-based reliability model method for real-time detection of early chatter in high-speed milling

et al. 2021

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Real-time detection of early chatter is a vital strategy to improve machining quality and material removal rate in the high-speed milling processes. This paper proposes a maximum entropy (MaxEnt) feature-based reliability model method for real-time detection of early chatter based on multiple sampling per revolution (MSPR) technique and second-order reliability method (SORM). To enhance the detection reliability, the MSPR is used to acquire multiple sets of once-per-revolution sampled data (i.e., MSPR data) and to overcome the shortcoming of the once-per-revolution sampling. The proposed MaxEnt feature-based reliability model method solves the issue of the real-time detection of early chatter while ensuring its reliability. The failure hazard function (FHF) is estimated as a chatter indicator by using the SORM with the MaxEnt feature. The proposed method consists of five steps. First, set the prior parameters. Then collect data by using the MSPR technique. Next, calculate a set of the standard deviation of the data collected as a chatter feature and estimate the chatter indicator FHF by applying the SORM with the MaxEnt feature. Finally, implement the real-time detection of early chatter based on the estimated chatter indicator FHF and the threshold FHF0. The proposed method is applied to the high-speed milling process. Two examples prove that the proposed method can detect two kinds of early chatter: the early-stage of a severe chatter and the slightly intolerable chatter.

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Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

MaxEnt feature-based reliability model method for real-time detection of early chatter in high-speed milling

et al. 2021

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“…The third method needs to fully consider the operating characteristics of the monitored objects because different objects have different mechanical structures, operating environments, and damage mechanisms. In terms of building degradation trend prediction models, most researchers use shallow machine learning methods, such as support vector machine (SVM) and extreme learning machine (ELM) [21][22][23][24], or the long short-term memory (LSTM) neural network, gating recurrent unit (GRU) model, and deep learning method optimized on this basis [25][26][27][28][29][30][31][32]. However, it is difficult for shallow machine learning methods to fully exploit the correlation between data, which will have a great impact on prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Contact Fatigue Performance Degradation Trends Based on Multi-Domain Features and Temporal Convolutional Networks

Liu,

Yang

2023

Entropy

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Contact fatigue is one of the most common failure forms of typical basic components such as bearings and gears. Accurate prediction of contact fatigue performance degradation trends in components is conducive to the scientific formulation of maintenance strategies and health management of equipment, which is of great significance for industrial production. In this paper, to realize the performance degradation trend prediction accurately, a prediction method based on multi-domain features and temporal convolutional networks (TCNs) was proposed. Firstly, a multi-domain and high-dimensional feature set of vibration signals was constructed, and performance degradation indexes with good sensitivity and strong trends were initially screened using comprehensive evaluation indexes. Secondly, the kernel principal component analysis (KPCA) method was used to eliminate redundant information among multi-domain features and construct health indexes (HIs) based on a convolutional autoencoder (CAE) network. Then, the performance degradation trend prediction model based on TCN was constructed, and the degradation trend prediction for the monitored object was realized using direct multi-step prediction. On this basis, the effectiveness of the proposed method was verified using a bearing common-use data set, and it was successfully applied to performance degradation trend prediction for rolling contact fatigue specimens. The results show that using KPCA can reduce the feature set from 14 dimensions to 4 dimensions and retain 98.33% of the information in the original preferred feature set. The method of constructing the HI based on CAE is effective, and change processes versus time of the constructed HI can truly reflect the degradation process of rolling contact fatigue specimen performance; this method has obvious advantages over the two commonly used methods for constructing HIs including auto-encoding (AE) networks and gaussian mixture models (GMMs). The model based on TCN can accurately predict the performance degradation of rolling contact fatigue specimens. Compared with prediction models based on long short-term memory (LSTM) networks and gating recurrent units (GRUs), the model based on TCN has better performance and higher prediction accuracy. The RMS error and average absolute error for a prediction step of 3 are 0.0146 and 0.0105, respectively. Overall, the proposed method has universal significance and can be applied to predict the performance degradation trend of other mechanical equipment/parts.

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“…Principal components analysis (PCA) is one of the most widely used data dimensionality reduction algorithms. It transforms high-dimensional features into low-dimensional features by reconstructing features, and has important applications in equipment performance evaluation [9]. Statistical Pattern Recognition (SPR) is a basic pattern recognition method.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Equipment Performance for Altitude Test Facility Flight Environment Simulation System

2022

Proceedings of 2022 the 12th International Workshop on Computer Science and Engineering

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Based on the predictive maintenance requirements of aero-engine Altitude Test Facility (ATF) equipment, a test equipment performance evaluation method (PCA-SPR) combining principal component analysis (PCA) and statistical pattern recognition (SPR) is proposed. It uses the PCA method to reduce the dimensionality of the equipment features, and recognizes the equipment status based on SPR. Then it defines the equipment health indicators based on the sample similarity theory, and achieves the quantitative evaluation of the equipment status. This paper takes the special control valve of ATF as the research object. Based on the simulation platform of the ATF flight environment simulation system, the valve performance degradation mode is studied and the valve degradation simulation is performed. The proposed PCA-SPR algorithm is applied to the valve performance evaluation to verify the applicability of the algorithm.

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Average Accumulative Based Time Variant Model for Early Diagnosis and Prognosis of Slowly Varying Faults

Cited by 9 publications

References 52 publications

MaxEnt feature-based reliability model method for real-time detection of early chatter in high-speed milling

MaxEnt feature-based reliability model method for real-time detection of early chatter in high-speed milling

Prediction of Contact Fatigue Performance Degradation Trends Based on Multi-Domain Features and Temporal Convolutional Networks

Evaluation of Equipment Performance for Altitude Test Facility Flight Environment Simulation System

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