Efficient Feature Learning Approach for Raw Industrial Vibration Data Using Two-Stage Learning Framework

Tnani, Mohamed-Ali; Subarnaduti, Paul; Diepold, Klaus

doi:10.3390/s22134813

Cited by 13 publications

(5 citation statements)

References 37 publications

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“…model that analyzes raw vibration data using Auto-Encoder as a feature extractor. A classification of 90.3% (F1-score) was achieved; however, the model training time was longer [21].…”

Section: Computational Resources In DLmentioning

confidence: 95%

A Comparative Study and Improved Bearing Fault Classifier Using Raw Vibration Data Under Limited Training Samples

Yap,

Lim,

Leong

2024

Int. J. Automot. Mech. Eng.

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Artificial intelligence is gaining traction in bearing fault detection and diagnosis. Generally, signal processing and feature selection are carried out to facilitate the fault classification process; however, classification accuracy tends to degrade under limited training data. In this paper, various artificial intelligence (AI) classification models are studied and compared using raw vibration data without signal processing and feature engineering. A Cosine k-Nearest Neighbours (CosKNN)-based classification model is optimized by integrating a Segmentive Mechanism, resulting in an overall classification F1-score improvement to 90.8% compared to the original classifier's 76.9%. The comparative findings show that the proposed model is suitable for circumstances with limited availability of training data, signal processing tools, and feature engineering tuning.

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“…model that analyzes raw vibration data using Auto-Encoder as a feature extractor. A classification of 90.3% (F1-score) was achieved; however, the model training time was longer [21].…”

Section: Computational Resources In DLmentioning

confidence: 95%

A Comparative Study and Improved Bearing Fault Classifier Using Raw Vibration Data Under Limited Training Samples

Yap,

Lim,

Leong

2024

Int. J. Automot. Mech. Eng.

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“…These approaches overcome the problem by adapting pre-trained models to extract deep features from the limited available signal samples, eliminating the need for model training. Recent trends utilize few-shot learning (FSL) [384]- [387], enabling the pre-trained models to generalize over new categories of data using only a few labeled samples per class. While DL-based solutions show promising results, they are computationally intensive.…”

Section: B Availability Of Labeled Abnormal Samplesmentioning

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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“…Well-known metric-based meta-learning networks include prototypical networks [21][22][23][24][25], relation networks [26,27], and matching networks [14].…”

Section: Metric-based Meta-learningmentioning

confidence: 99%

Few-Shot Learning Approaches for Fault Diagnosis Using Vibration Data: A Comprehensive Review

Liang,

Zhang,

Feng

et al. 2023

Sustainability

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Fault detection and diagnosis play a crucial role in ensuring the reliability and safety of modern industrial systems. For safety and cost considerations, critical equipment and systems in industrial operations are typically not allowed to operate in severe fault states. Moreover, obtaining labeled samples for fault diagnosis often requires significant human effort. This results in limited labeled data for many application scenarios. Thus, the focus of attention has shifted towards learning from a small amount of data. Few-shot learning has emerged as a solution to this challenge, aiming to develop models that can effectively solve problems with only a few samples. This approach has gained significant traction in various fields, such as computer vision, natural language processing, audio and speech, reinforcement learning, robotics, and data analysis. Surprisingly, despite its wide applicability, there have been limited investigations or reviews on applying few-shot learning to the field of mechanical fault diagnosis. In this paper, we provide a comprehensive review of the relevant work on few-shot learning in mechanical fault diagnosis from 2018 to September 2023. By examining the existing research, we aimed to shed light on the potential of few-shot learning in this domain and offer valuable insights for future research directions.

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Efficient Feature Learning Approach for Raw Industrial Vibration Data Using Two-Stage Learning Framework

Cited by 13 publications

References 37 publications

A Comparative Study and Improved Bearing Fault Classifier Using Raw Vibration Data Under Limited Training Samples

A Comparative Study and Improved Bearing Fault Classifier Using Raw Vibration Data Under Limited Training Samples

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

Few-Shot Learning Approaches for Fault Diagnosis Using Vibration Data: A Comprehensive Review

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