Utilizing AI Techniques to Detect Anomalies for Predictive Maintenance in the Mining Industry

Artificial intelligence (AI) and Machine Learning (ML) can be essential in improving efficiency and reducing the cost of operations in the mining industry. Unlike traditional reactive methods, AI and ML-based techniques enable a proactive approach that uses sensor data and historical information to predict equipment failures. This helps prevent costly equipment downtime.

This paper delves into the application of AI and ML techniques, particularly in anomaly detection, to enhance safety and efficiency in the mining industry. It explores various anomaly detection methods, including statistical, rule-based, machine learning, and deep learning approaches, highlighting their strengths and weaknesses. It presents an ML-based approach and underlines the pivotal role of the audience’s domain expertise and good quality data in its successful implementation. The paper also showcases various use cases of anomaly detection that Infosys has successfully implemented in this industry using AI/ML techniques, demonstrating the end user’s integral role in these advancements. It also shares the benefits realized through these implementations, such as improved efficiency, enhanced safety, and reduced costs, further validating the end user’s expertise.

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