Department of Networking and Communications, College of Engineering and Technology, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India.
Machinery Fault Detection (MFD) is an important process in contemporary industrial systems, where it predicts possible physical failures before they lead to a serious problem. This uses multiple technologies to monitor machine statuses (algorithms, data gathering systems and sensors) Using a servo-motor driven actuator for deployment, the Locking Mechanism is pre-assembled into an OEM ATE and will enable predictive failure mode identification (via monitoring and warnings of operational parameters i.e., vibration, temperature or auditory signals in-built to MFD systems) leading to Prophylactic maintenance before critical bottlenecks can occur. The dataset we used in our study was collected from Kaggle and it is called the SpectraQuest Machinery Fault Simulator (MFS) Alignment-Balance-Vibration (ABVT). We used LSTM Autoencoder, KNN, SVM and DNN to analyzed the data. Our LSTM Autoencoder model was very accurate and achieved a precision, recall, accuracy and F-score of 99%. We worked on very large scale datasets. It will help the system detect faults and predict their evolution over time, so you save maintenance costs and increase production in your factory. More research on the practical efficiency of these models in real-time across different industrial settings can create a path towards improved and scalable MFD solutions.
Keywords
Machinery Fault Detection (MFD), Industrial Maintenance, Machine Learning in Fault Detection, Fault Detection, Deep Learning, LSTM Autoencoder.
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Ranjith Kumar Painam
Ranjith Kumar Painam
Department of Electronics and Communication Engineering, Kallam Haranadhareddy Institute of Technology (Autonomous), Guntur, Andhra Pradesh, India.
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Cite this article
Rupa Devi B, Suseela G, Ranjith Kumar Painam, Thammisetty Swetha, Suryanarayana G
and Reddy Madhavi K, “Intelligent Fault Diagnosis in Industrial Machinery: Leveraging AI with
LSTM Autoencoder for Enhanced Fault Detection”, Journal of Machine and Computing, pp. 931-942,
October 2024. doi:10.53759/7669/jmc202404086.
