Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection

Tian, Anmin; Fu, Chengbiao; Li, Yu-Chung; Yau, Her‐Terng

doi:10.3390/s18093069

Cited by 11 publications

(5 citation statements)

References 40 publications

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“…First, 36 records are made at 48,828 Hz of bearings with normal operation status as shown in TABLE IV, the signals were all recorded for the same length of time. A model was created using CNN from data extracted by STFT, and the Chen-Lee Chaotic and Lorenz Chaotic Systems [35][36][37]. After this a comparison was made of each approach to find which gave the best accuracy rate and efficiency.…”

Section: Equipment and Experimentalmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Chaotic Dynamic Errors in Key Components

Jian

Yau

2021

IEEE Access

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Ball bearings are one of the most common components used in rotating machines. They reduce the rotational friction between the shaft and fixed components and maintain the center line of rotation of the shaft. A damaged bearing will cause abnormal vibration and noise, and often results in machine failure and loss of production. In this study the public database on ball bearings, provided by the Vibration Institute of Machinery Failure Prevention Technology (MFPT), was used for data retrieval and analysis, and a diagnosis model was created according to the data sets of the bearing in the database. Three different approaches were used for the extraction of features and a classifier was used to implement a diagnostic system. The aim of this study was a comparison of three approaches. The first was the Short-time Fourier Transform (STFT) where the time-frequency domain image is extracted as the feature used for status identification. The second and third approaches were based on the Chen-Lee Chaotic and the Lorenz Chaotic Systems and chaotic dynamic error maps were used in analysis and feature status identification. Chaotic systems are particularly sensitive to the slightest changes in input signals, and the time domain signals from bearings in different conditions were mapped onto individual images. The feature images extracted by the three different approaches were then used for training and verification in a Convolutional Neural Network (CNN). From the results of the experiments, it can be seen that all three approaches gave high identification rates. The interactive verification identification rate of the Chen-Lee chaotic system with CNN under three statuses reached 98.33%, and it also had the best computational efficiency in the condition without losing any classification accuracy. This will make a substantial contribution to real-time ball bearing fault diagnosis.

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Section: Equipment and Experimentalmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Chaotic Dynamic Errors in Key Components

Jian

Yau

2021

IEEE Access

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“…In order to extend a specific set into a continuous value range (−∞, ∞) and calculate the correlation degree, the extensive set is established [37]. The extensive set E(T) is required which are defined as…”

Section: Extension Setsmentioning

confidence: 99%

Research on Ride Comfort and Driving Safety under Hybrid Damping Extension Control for Suspension Systems

Geng

Sun

et al. 2020

Applied Sciences

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This paper is concerned with the conflicting performances of ride comfort and driving safety for semi-active suspension systems. To alleviate this conflict, a novel hybrid damping extension control (HDEC) method is proposed. This method adopts various control methods and the weights of each method are determined by extension theory. Firstly, body acceleration and tire dynamic transformation are selected to evaluate ride comfort and driving safety performance for the semi-active suspension system and their frequency responses of passive suspension, sky-hook control, ground hook control, and S-GH (sky-ground hook) control are analyzed based on a two degree-of-freedom (2-DOF) model. Secondly, extension theory is introduced and the extension control system, which contains three modes and corresponding control algorithms, is established. In addition, the low-frequency excitation and high-frequency excitation simulations are designed to determine the parameters of the extension control system. Finally, ve-DYNA vehicle suspension model simulation is applied to prove the feasibility and effectiveness of the extension control. The simulation results show that, based on the suspension state, extension control can improve the performance of ride comfort and driving safety.

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“…Kuo [22] used chaos synchronization theory to diagnose the faults in ball bearings. Tian [23] used fractional lorenz chaos extension detection to detect the intelligent ball bearing fault. Yan [24] carried out chaotic feature extraction for early fault diagnosis of rolling bearing.…”

Section: Introductionmentioning

confidence: 99%

Rub-impact fault identification of a bladed rotor based on chaotic features

Kou

Yue

Lee

et al. 2023

Preprint

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The bladed rotor is an important part in turbine machines. Timely detection of its blade rubbing fault may avoid serious accidents. This paper developed a rub-impact fault identification approach for the bladed rotor based on chaotic features such as average period, time delay, embedded dimension, and largest Lyapunov exponent. A dynamic analytical model of a rotating variable thickness blade with the rub-impact fault is established. It is verified that the blade rubbing model could obtain the relationship between the chaotic behaviors and the rub-impact fault for a rotating blade. Through the nonlinear characteristic analysis, we could establish that different blade rubbing states have a certain corresponding relationship with the chaotic characteristics, which provides the theoretical basis for the blade rubbing identification using the chaotic features. In particular, a rub-impact fault identification approach based on chaotic features is further studied. And the blade rubbing fault can be easily identified by combining four chaotic features, which is proven by experiments. Comparing with the time domain and frequency domain analysis methods, the proposed approach provides a new way to identify such blade rubbing fault.

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Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection

Cited by 11 publications

References 40 publications

Bearing Fault Diagnosis Based on Chaotic Dynamic Errors in Key Components

Bearing Fault Diagnosis Based on Chaotic Dynamic Errors in Key Components

Research on Ride Comfort and Driving Safety under Hybrid Damping Extension Control for Suspension Systems

Rub-impact fault identification of a bladed rotor based on chaotic features

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