Rolling Bearing Fault Diagnosis Based on the Coherent Demodulation Model

Shao, Yinghua; Kang, Rui; Liu, Jie

doi:10.1109/access.2020.3038767

Cited by 11 publications

(5 citation statements)

References 44 publications

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“…A fundamental tenet of bearing fault diagnosis is that training data from a source domain and test data from a target domain follow the same distribution. However, [5] The study [7] employs the coherent demodulation principle, offering a revolutionary approach to fault-bearing diagnosis.…”

Section: Review Of the Related Workmentioning

confidence: 99%

Data-Driven Intelligent Condition Adaptation of Feature Extraction for Bearing Fault Detection Using Deep Responsible Active Learning

Mahesh,

Chandrasekaran,

Ram

et al. 2024

IEEE Access

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The detection of faulty bearings is an essential step in guaranteeing the safe and efficient operation of rotating machinery. Bearings, which also transmit the loads and pressures generated by the machinery, support the rotating shafts. A common method for bearing fault diagnostics is using signal processing techniques. In terms of accuracy, dependability, and sensitivity to various fault types and severity levels, these techniques do, however, have significant limits. To address these limitations, practitioners often integrate signal processing with advanced techniques like machine learning and data analytics to enhance diagnostic accuracy, reliability, and overall effectiveness in bearing fault detection and predictive maintenance. Exploration of various models has demonstrated enhanced results in managing nonlinear data to a certain degree; however, these models face challenges when dealing with intricately complex patterns. Moreover, CNNs can automatically learn relevant features from raw sensor data, capturing intricate patterns and relationships in the data without the need for manual feature engineering. CNNs can be optimized for scalability and real-time processing, essential for applications requiring quick decisions and computationally less expensive for large datasets compared to other models. An optimized one-dimensional Convolutional Neural Network (1D CNN) using different kernel sizes is proposed for predicting and finding bearing problems to overcome these constraints. This method creates a feature vector by applying many filters of various sizes to the input signal. Using the created feature vector, the input signal can be divided into many categories, such as healthy or unhealthy. In comparison to other methods, the proposed technology performs better and offers a high accuracy of 99.52% in bearing fault identification. The 1D CNN model with multiple kernel sizes excels in preserving data structure during dimensionality reduction, as confirmed by comparing t-Distributed Stochastic Neighbor Embedding plots. Particularly, the optimized 1D CNN with multiple kernel sizes accurately classifies faults with minimal errors, showcasing its fault classification proficiency compared with the other state-of-the-art methods. The visualization underscores the methodology's efficacy in discerning intricate fault patterns within the data.

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Section: Review Of the Related Workmentioning

confidence: 99%

Data-Driven Intelligent Condition Adaptation of Feature Extraction for Bearing Fault Detection Using Deep Responsible Active Learning

Mahesh,

Chandrasekaran,

Ram

et al. 2024

IEEE Access

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“…Rolling bearings are the most widely used parts in various industrial equipment [1][2][3][4]. However, due to the harsh service environment, it easily leads to various faults of rolling bearings, which will directly affect the safe operation of the whole system [5][6][7][8]. Therefore, timely diagnosis of bearing failures is important to ensure the whole mechanical system operating in an efficient and safe condition.…”

Section: Introductionmentioning

confidence: 99%

Incipient Bearing Fault Extraction based on an Adaptive Multi-stage Noise Reduction Method

Tian,

Feng,

Meng

et al. 2024

J. Phys.: Conf. Ser.

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Considering the strong nonlinear and non-stationary characteristics of rolling bearing vibration signals, this paper proposes a multi-stage noise reduction method using adaptive variational mode decomposition and modulation signal bispectrum (AVMD-MSB) to extract the fault features of rolling bearings. Firstly, the AVMD is employed to adaptively select VMD parameters K and α and decompose the signal into a series of Intrinsic mode functions (IMFs), which allows an adaptive selection of the parameters of VMD. Then, all IMF components are reconstructed with weights according to the index of correlation kurtosis to avoid accidental omission of the IMFs containing important fault information. Finally, MSB is implemented to further suppress residual noises and interference components in the signal, precisely extract the bearing fault features. Numerical simulation and case study show that the AVMD-MSB is more advantageous in extracting fault characteristics from rolling bearing vibration signals compared with AVMD-Envelope and conventional VMD-MSB.

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“…This model considers that the bearing's health status cannot be directly observed but can only be inferred from observable data, such as vibration signals or acoustic emissions. The HMM assumes that the bearing's health status follows a hidden state process that can be modelled by a Markov chain, and the observable data is generated based on the current hidden state [17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Applying Stochastic Models to Analyze Vibration Features of Roller Bearings

Bhatti,

Shaikh,

Mansoor

et al. 2023

Preprint

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Machinery parts gradually wear out over time due to regular usage. To improve machinery health and prevent critical issues, a reliable prognosis framework can be implemented by monitoring the behaviour of machinery parts and issuing warnings before they reach a critical state. To achieve this, vibration data from roller bearings experiencing various fault conditions have been collected. Different techniques from the literature were combined to analyze the distinct configurations in the vibration data sets and identify the main defects in roller bearings. The significant features extracted from this analysis were then used to create optimized stochastic model equations, separately regressing inner and outer race fault features to healthy bearing features under random conditions. These models can help engineers design more dependable systems, optimize their performance, and minimize the risk of failures and downtime.

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Rolling Bearing Fault Diagnosis Based on the Coherent Demodulation Model

Cited by 11 publications

References 44 publications

Data-Driven Intelligent Condition Adaptation of Feature Extraction for Bearing Fault Detection Using Deep Responsible Active Learning

Data-Driven Intelligent Condition Adaptation of Feature Extraction for Bearing Fault Detection Using Deep Responsible Active Learning

Incipient Bearing Fault Extraction based on an Adaptive Multi-stage Noise Reduction Method

Applying Stochastic Models to Analyze Vibration Features of Roller Bearings

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