Abstract:

Predictive maintenance is a crucial aspect of ensuring the reliability and cost-effectiveness of equipment in the energy sector. By leveraging machine learning techniques, companies can predict equipment failures and optimize maintenance schedules, reducing both downtime and operational costs. However, existing methods often rely on traditional maintenance schedules or basic machine learning models that struggle to effectively handle complex, large-scale datasets with limited labeled data. The proposed method, Anomaly Detection using Unsupervised Learning (AD-UL), addresses these challenges by identifying irregular patterns in equipment behavior without the need for labeled failure data. This technique helps detect potential failures before they occur, providing early warnings of anomalies and enabling proactive maintenance actions. By implementing the AD-UL framework, energy companies can enhance predictive maintenance strategies, improve equipment reliability, and reduce costs. The results indicate that this approach outperforms existing methods by detecting failures more accurately and earlier, leading to significant cost savings and improved asset management