Author(s): Torres, Pedro ; Spencer, Geoffrey ; Esteves, Arthur ; Sousa, Fernando ; Pereira, Fernando J.G. ; Guerreiro, Rui M.L.
Date: 2025
Persistent ID: http://hdl.handle.net/10400.11/10338
Origin: Repositório Científico do Instituto Politécnico de Castelo Branco
Subject(s): Condition monitoring; Anomaly detection; Vibration analysis; Unsupervised learning; Data scarcity; LSTM Autoencoder
The integration of the use of Artificial Intelligence (AI) in industrial environments often faces the lack of labeled data, as well as historical records. This lack of information becomes a problem when implementing predictive maintenance solutions, particularly in monitoring the condition of industrial machines and automatic fault detection. This work addresses this issue in an industrial scenario, through the analysis of vibrations in a spindle motor of an ornamental stone cutting machine. Unsupervised learning techniques are explored for anomaly detection through vibration data, using the training and implementation of an LSTM (Long Short-Term Memory) Autoencoder model. Datasets consist only of unlabeled accelerometer signals acquired during normal machine operation. An analysis based on the extraction of statistical features from the signal is adopted to use them as inputs of the Machine Learning algorithm, to learn the normal behavior of the machine and detect deviations that may correspond to potential anomalies. The experimental results show that even in the absence of labeled data, it is possible to extract meaningful insights from the machine state and establish a practical pipeline for anomaly detection in industrial machines through vibration analysis.
