Predictive Maintenance for Interactive Art Installations

Interactive art installations are a combination of creativity and technology to interact with audiences by responding to environmental and user stimulation. The challenge is however, keeping these systems up to date since they are installed with sophisticated hardware and software modules which may fail or deteriorate at any time. Conventional maintenance approaches such as reactive or planned maintenance are usually accompanied by downtimes and higher expenses and shorter audience attendance. The paper presents a predictive maintenance framework that would be effective with interactive art installations. The framework puts together sensors, IoT devices and machine learning models to continuously ensure the health of the system in real time and predict any form of failure. The system architecture proposed incorporates modules in hardware acquisition and communication modules to remotely monitor and analytics modules to process sensor data. Vibration sensors, temperature probes, and current monitors are some of the important technology used to record the appropriate operational data. The performance of different machine learning models, such as Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, and Random Forests, is compared in terms of their performance in terms of the anomaly detection and failure predictability. One of the case studies illustrates the implementation of this predictive maintenance system in the interactive kinetic sculpture, where the focus is on the data collection procedure, feature extraction, and model analysis

Anomaly detection · Predictive maintenance · Preventive maintenance · Art, Technology, and Culture · Interactive and Immersive Displays · Music Technology and Sound Studies · Software