Enhancing Machine Reliability in Industrial Plants Leveraging Diagnostic and Prognostic Approach to measure reliability improvements

Abstract

In the dynamic and demanding environment of industrial plants, the reliability of machines is paramount. Ensuring that machinery operates reliably and efficiently is crucial for profitability of the plant. Reliability in industrial plants is beyond preventing failures and also about enhancing performance and extending the lifespan of equipment. By focusing on the most failure-prone components or systems, maintenance teams can prioritize their efforts and resources effectively, leading to significant improvements in overall reliability and total cost of ownership. This abstract delves into the critical role of reliability in industrial environments, emphasizing the importance of employing reliability growth models to systematically validate the effectiveness of solutions implemented to address machine reliability issues.

For every unplanned events(trips), remote real-time data gathering and analysis conducted to identify the components or systems responsible for the trip. All the events and contributors are tracked and trended to identify top offenders. Identified top offenders are deeply investigated to find the solution & opportunity to develop the automatic diagnostic and prognostic tool based on remotely acquired time-series data. Based on outcome of Diagnostic and prognostic tools, identifying the degradation of equipment.  Once a malfunction is identified, we analyze root causes, extract learnings, and develop targeted improvements. These improvements are first validated in controlled environments (e.g., lab or test bench), then implemented incrementally across the fleet. Each implementation cycle is tracked using reliability growth models to statistically measure the reduction in failure rates and validate the effectiveness of the solution over time.

This process allows us to Diagnose and isolate malfunctions using real-time analytics, Generate and refine new analytics based on observed failure modes, Quantify reliability growth through Mean Time between failure (MTBF) decrease / Mean Time Between Trip (MTBT) improvements, Scale validated solutions from individual assets to the entire fleet.

By integrating reliability growth models into our reliability process, we ensure that each improvement is measured and also predictively estimate the reliability improvement on any other unit in the fleet. This methodology has already demonstrated success, with MTBT improvements from 1,000 to 8,000 hours, showcasing the power of structured reliability growth modelling in complex, distributed systems.