Assessment of IoT-Driven Predictive Maintenance Strategies for Computed Tomography Equipment: A Machine Learning Approach

• Published in: IEEE access 2024, Vol.12, p.195505-195515
• Main Authors: Habib Shah Ershad Mohd Azrul Shazril, Mohammad, Mashohor, Syamsiah, Effendi Amran, Mohd, Fatinah Hafiz, Nur, Mohd Ali, Azizi, Saiful Bin Naseri, Mohd, Rasid, Mohd Fadlee A.
• Format: Article
• Language: English
• Subjects: Accuracy; artificial neural network; Artificial neural networks; Breakdown; Breakdowns; Computed tomography; CT-scan; Data models; Downtime; Electric breakdown; Internet of Things; IoT; Machine learning; Maintenance; Medical devices; Medical equipment; Noise generators; Noise prediction; Normal distribution; Predictive maintenance; Predictive models; Random noise; Sensors; Synthetic data; Temperature sensors; Tomography
• ISSN: 2169-3536
• DOI: 10.1109/ACCESS.2024.3518516

Predictive maintenance (PdM) identifies the equipment conditions and forecasts when maintenance is required to minimize downtime, whis is crucial for medical equipment. This study developed a machine learning-based PdM for a Computed Tomography (CT) scan machine using Internet of Things (IoT) sensors to monitor temperature, humidity, current, radiation, and XY-axis acceleration. Data were collected from January to December 2023 at a hospital in the Klang Valley, Malaysia. The readings were preprocessed to follow a normal distribution, representing the typical working conditions of the machine. Owing to limited faulty condition data, synthetic data were generated by expanding the tails of the data distribution and using a Gaussian noise generator. These synthetic data are vital for training robust machine learning models. An artificial neural network (ANN) was designed to predict the machine's breakdown risk using all sensor parameters as inputs. The ANN model achieved an impressive prediction accuracy of 97.58%, proving its relibility in forecasting breakdowns. The model consistently predicted a high breakdown risk in November 2023, which was confirmed by a repair report that indicating maintenance was required in early December 2023. This study demonstrated that integrating IoT sensors with ANN models can significantly enhance the PdM of medical equipment, reduce downtime, and improve operational efficiency. These promising results suggest the potential application of this approach in other critical medical devices.