Similarity-Based Predictive Maintenance Framework for Rotating Machinery

TL;DR

This paper proposes a similarity-based predictive maintenance framework for rotating machinery that effectively classifies operational states using limited labeled data, outperforming traditional machine learning methods in accuracy and computational efficiency.

Contribution

Introduces a novel similarity-based framework for machinery condition monitoring that reduces dependence on large labeled datasets and demonstrates superior performance over ML-based approaches.

Findings

High classification accuracy achieved with limited labeled data

FFT features combined with cosine similarity outperform other configurations

Similarity-based methods require moderate computational resources

Abstract

Within smart manufacturing, data driven techniques are commonly adopted for condition monitoring and fault diagnosis of rotating machinery. Classical approaches use supervised learning where a classifier is trained on labeled data to predict or classify different operational states of the machine. However, in most industrial applications, labeled data is limited in terms of its size and type. Hence, it cannot serve the training purpose. In this paper, this problem is tackled by addressing the classification task as a similarity measure to a reference sample rather than a supervised classification task. Similarity-based approaches require a limited amount of labeled data and hence, meet the requirements of real-world industrial applications. Accordingly, the paper introduces a similarity-based framework for predictive maintenance (PdM) of rotating machinery. For each operational state of the machine, a reference vibration signal is generated and labeled according to the machine's operational condition. Consequentially, statistical time analysis, fast Fourier transform (FFT), and short-time Fourier transform (STFT) are used to extract features from the captured vibration signals. For each feature type, three similarity metrics, namely structural similarity measure (SSM), cosine similarity, and Euclidean distance are used to measure the similarity between test signals and reference signals in the feature space. Hence, nine settings in terms of feature type-similarity measure combinations are evaluated. Experimental results confirm the effectiveness of similarity-based approaches in achieving very high accuracy with moderate computational requirements compared to machine learning (ML)-based methods. Further, the results indicate that using FFT features with cosine similarity would lead to better performance compared to the other settings.