Failure prediction is a research area that focuses on developing algorithms and techniques to predict when machines, systems, or processes are likely to fail. By analyzing data such as sensor readings, historical maintenance records, and other relevant information, researchers aim to identify patterns and indicators that can be used to forecast when a failure is likely to occur. This information can be used to optimize maintenance schedules, reduce downtime, and improve overall system reliability and performance. Machine learning and data mining techniques are often used in failure prediction research to extract meaningful insights from large and complex datasets.