A novel based-performance degradation indicator RUL prediction model and its application in rolling bearing

2021 ◽  
Author(s):  
Chuangyan Yang ◽  
Jun Ma ◽  
Xiaodong Wang ◽  
Xiang Li ◽  
Zhuorui Li ◽  
...  
Keyword(s):  
Prediction Model ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Degradation Indicator

Rolling bearing degradation condition clustering using multidimensional degradation feature and Gath–Geva fuzzy clustering algorithm

2020 ◽  
pp. 107754632095495
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Tao X Mei ◽  
Sun D Jian ◽  
Wang Wei
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chaotic Map ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Time Parameter ◽  
Mechanical Equipment ◽  
Time Dimension

In allusion to the issue of rolling bearing degradation feature extraction and degradation condition clustering, a logistic chaotic map is introduced to analyze the advantages of C0 complexity and a technique based on a multidimensional degradation feature and Gath–Geva fuzzy clustering algorithmic is proposed. The multidimensional degradation feature includes C0 complexity, root mean square, and curved time parameter which is more in line with the performance degradation process. Gath–Geva fuzzy clustering is introduced to divide different conditions during the degradation process. A rolling bearing lifetime vibration signal from intelligent maintenance system bearing test center was introduced for instance analysis. The results show that C0 complexity is able to describe the degradation process and has advantages in sensitivity and calculation speed. The introduced degradation indicator curved time parameter can reflect the agglomeration character of the degradation condition at time dimension, which is more in line with the performance degradation pattern of mechanical equipment. The Gath–Geva fuzzy clustering algorithmic is able to cluster degradation condition of mechanical equipment such as bearings accurately.

scholarly journals Performance Degradation Assessment of Concrete Beams Based on Acoustic Emission Burst Features and Mahalanobis—Taguchi System

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3402 ◽  
Author(s):  
Md Arafat Habib ◽  
Akhand Rai ◽  
Jong-Myon Kim
Keyword(s):  
Acoustic Emission ◽  
Concrete Beams ◽  
Early Stage ◽  
Concrete Structures ◽  
Feature Selection Method ◽  
Noise Removal ◽  
Performance Degradation ◽  
Reinforced Concrete Beams ◽  
Chebyshev Inequality ◽  
Degradation Indicator

Acoustic emission (AE) has been used extensively for structural health monitoring based on the stress waves generated due to evolution of cracks in concrete structures. A major concern while using AE features is that each of them responds differently to the fractures in concrete structures. To tackle this problem, Mahalanobis—Taguchi system (MTS) is utilized, which fuses the AE feature space to provide comprehensive and reliable degradation indicator with a feature selection method to determine useful features. Further, majority of the existing investigations gave little attention to naturally occurring cracks, which are actually more difficult to detect. In this study, a novel degradation indicator (DI) based on AE features and MTS is proposed to indicate the performance degradation in reinforced concrete beams. The experimental results confirm that the MTS can successfully distinguish between healthy and faulty conditions. To alleviate the noise from the DI obtained through MTS, a noise-removal strategy based on Chebyshev inequality is suggested. The results show that the proposed DI based on AE features and MTS is capable of detecting early stage cracks as well as development of damage in concrete beams.

A degradation phase division technique based on Gath–Geva fuzzy clustering

2020 ◽  
Vol 26 (15-16) ◽  
pp. 1147-1154
Author(s):  
Bing Wang ◽  
Wang Wei ◽  
Xiong Hu ◽  
Dejian Sun
Keyword(s):  
Fuzzy Clustering ◽  
Chaotic Map ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Mechanical Equipment ◽  
Spectral Entropy ◽  
Variation Pattern ◽  
Intelligent Maintenance

In allusion to the issue of degradation feature extraction and degradation phase division, a logistic chaotic map is used to study the variation pattern of spectral entropy, and a technique based on Gath–Geva fuzzy clustering is proposed. The degradation features include spectral entropy, root mean square, and “curved time,” which are more in line with the performance degradation process than degradation time. Gath–Geva fuzzy clustering is introduced to divide different phases in the degradation process. The rolling bearing lifetime vibration signal from the intelligent maintenance systems (IMS) bearing test center was introduced for instance analysis. The results show that spectral entropy is able to effectively describe the complexity variation pattern in the performance degradation process and has some advantages in sensitivity and calculation speed. The introduced “curved time” is able to reflect the agglomeration character of the degradation condition on a time scale, which is more in line with the performance degradation pattern of mechanical equipment. Gath–Geva fuzzy clustering is able to divide the degradation phase of mechanical equipment such as bearings accurately.

Performance degradation assessment of rolling element bearings based on hierarchical entropy and general distance

2017 ◽  
Vol 24 (14) ◽  
pp. 3194-3205 ◽  
Author(s):  
Keheng Zhu
Keyword(s):  
Degradation Process ◽  
Performance Degradation ◽  
Rolling Element Bearings ◽  
Vibration Signals ◽  
Feature Vectors ◽  
Multi Scale ◽  
Nonlinear Dynamic Characteristics ◽  
Rolling Element ◽  
Bearing Vibration ◽  
Degradation Indicator

Performance degradation assessment is crucial to realize equipment’s near-zero downtime and maximum productivity. In this paper, a new method for performance degradation assessment of rolling element bearings is proposed based on hierarchical entropy (HE) and general distance. First, considering the nonlinear dynamic characteristics of bearing vibration signals, the HE method is utilized to extract feature vectors, which can obtain more bearing state information hidden in the vibration signals than sample entropy (SampEn) and multi-scale entropy (MSE). Then, the general distance between the feature vectors of the normal data and those of the tested data is designed as a degradation indicator by combining Euclidean distance and cosine angle distance. The experimental results indicate that this indicator can detect the incipient defects well and can effectively reflect the whole degradation process of rolling element bearings. Moreover, the designed indicator has some advantages over kurtosis and root mean square (RMS) values.

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