Innovative predictive maintenance for mining grinding mills: from LSTM-based vibration forecasting to pixel-based MFCC image and CNN

This article presents an innovative predictive maintenance for grinding mills, aiming to enhance operational efficiency and minimize downtime. Leveraging advancements in data analytics and IoT sensor technologies, the approach integrates vibration signal forecasting, LSTM-based fast Fourier transfor...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2024-11, Vol.135 (3-4), p.1271-1289
Main Authors: Rihi, Ayoub, Baïna, Salah, Mhada, Fatima-Zahra, El Bachari, Essaid, Tagemouati, Hicham, Guerboub, Mhamed, Benzakour, Intissar, Baïna, Karim, Abdelwahed, El Hassan
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
CAE) and Design
Computer-Aided Engineering (CAD
Continuous wavelet transform
Data analysis
Datasets
Engineering
Fast Fourier transformations
Fault detection
Forecasting
Fourier transforms
Grinding mills
Image enhancement
Industrial and Production Engineering
Mechanical Engineering
Media Management
Original Article
Performance prediction
Predictive maintenance
Vibration analysis
Wavelet transforms
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Summary:This article presents an innovative predictive maintenance for grinding mills, aiming to enhance operational efficiency and minimize downtime. Leveraging advancements in data analytics and IoT sensor technologies, the approach integrates vibration signal forecasting, LSTM-based fast Fourier transform (FFT) analysis, and convolutional neural networks (CNNs) to detect faults early on. The method involves creating LSTM models to forecast vibration signals based on historical data and using FFT analysis to identify fault frequencies associated with the grinding process. Additionally, techniques such as Mel-frequency cepstral coefficients (MFCCs), short-time Fourier transform (STFT), and continuous wavelet transform (CWT) are employed for spectrogram extraction, providing valuable insights into machinery conditions. Validation on real-world datasets with 99.95% of accuracy with 1 in AUC-ROC, showcases the robust predictive performance of the model and has reached 99.96% of accuracy for 16 classes with an AUC-ROC of 1 using CWRU dataset, surpassing existing approaches and demonstrating its potential for proactive maintenance across various industries beyond mining.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-024-14588-3