A Feature Extraction Method Using VMD and Improved Envelope Spectrum Entropy for Rolling Bearing Fault Diagnosis

Yang, Yang; Liu, Hui; Han, Lijin; Gao, Pu

doi:10.1109/jsen.2022.3232707

Cited by 51 publications

(13 citation statements)

References 36 publications

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“…In the existing literature, signal decomposition is utilized through two approaches for feature extraction; each approach serves a distinct objective: 1) Extraction of highly discriminative features: In these methods, features are extracted from elementary modes resulting from the decomposition process. Accordingly, feature extraction methods can be generally grouped under four main categories: entropy-based [262], [263], [267], [273], [274], [283], energy-based [251], [257], [264], [265], spectral-based [44], [252], [266], [272], [275], [280], [282], [283], and statistical-based [247],…”

Section: Signal Decomposition-based Methodsmentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.

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Section: Signal Decomposition-based Methodsmentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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“…When decomposing IMFs, traditional decomposition methods including empirical mode decomposition (EMD) frequently suffer from issues such as end effects, mode mixing, over-enveloping, and under-enveloping [4]. The adaptive signal processing method called VMD effectively mitigates mode mixing and boundary effects often encountered in traditional decomposition techniques [5]. Specifically, VMD is well-suited for analyzing nonstationary and nonlinear vibration signals.…”

Section: B Literature Reviewmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Optimized VMD and SSAE

Chang,

Zhao,

Guo

et al. 2024

IEEE Access

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The monitoring and fault diagnosis of axle-box bearings in high-speed trains is crucial for ensuring safe train operations. The vibration signals of these bearings exhibit non-stationary and non-linear characteristics. To further enhance the accuracy of identifying rolling bearing faults, a fault diagnosis method is proposed. This method is based on the improved Dung Beetle Optimization (DBO) algorithm for optimizing Variational Mode Decomposition (VMD) combined with Stacked Sparse Autoencoder (SSAE). Firstly, the DBO algorithm is enhanced to improve its optimization precision and global optimization capability. It is then utilized for the adaptive selection of two parameters: the number of decomposition modes and the penalty factor in VMD. These improvements address issues such as mode mixing, signal loss, and excessive decomposition, which arise from poor parameter selection in the traditional VMD method. Subsequently, components of intrinsic mode functions (IMFs) that are highly correlated with the original signal are selected. The time-domain and frequency-domain features of these IMF components are used to construct the dataset. The feature set is then inputted into the deep machine learning model SSAE for training and testing. Through diagnostic experiments on various types and levels of rolling bearing faults, the model demonstrates a higher rate of fault diagnosis recognition.INDEX TERMS Bearing fault diagnosis, dung beetle optimization algorithm, variational modal decomposition, stacked sparse autoencoders.

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“…Yang et al [3] presented an approach for bearing status feature extraction utilizing variational mode decomposition (VMD) and improved envelope spectrum entropy (IESE). The vibrational signals of the bearing are initially decomposed into different intrinsic mode functions (IMFs) by VMD.…”

Section: Literature Reviewmentioning

confidence: 99%

An MTBWO Algorithm Based on BiGRU Model

Yang,

Sun,

Zhang

2024

Electronics

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To address the challenge of distinguishing the health status of bearings, in this paper, a health index (HI) is developed through utilization of the multiple target time-varying black widow optimization–bidirectional gating recurrent unit (MTBWO-BiGRU) model and the Bray–Curtis distance. This index offers a visual representation of the health status of bearings, enabling more intuitive monitoring and prediction. The first step involves utilizing L1 regularization to extract effective features as degradation elements from the current bearing vibration data. Additionally, the characteristics of the initial time window of the vibration data serve as the health features. Next, the HI of the bearing is constructed by computing the Bray–Curtis distance between the bearing’s degradation characteristics and health features. The cloud monitoring platform constantly tracks the health of the bearing and employs the MTBWO-BiGRU model to anticipate the forthcoming state of health. The platform generates an immediate alert when the HI of the bearing overtakes the alteration rate threshold and foresees the condition of the bearing. We compare the MTBWO-BiGRU model with the bidirectional long short-term memory (BiLSTM) and BiGRU models. The results indicate an accuracy level of 92.57%, which is evidently higher than that obtained when using the other two models. Moreover, the MTBWO-BiGRU model is lighter, demonstrating the practicality of the proposed approach.

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A Feature Extraction Method Using VMD and Improved Envelope Spectrum Entropy for Rolling Bearing Fault Diagnosis

Cited by 51 publications

References 36 publications

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Rolling Bearing Fault Diagnosis Based on Optimized VMD and SSAE

An MTBWO Algorithm Based on BiGRU Model

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