Mouser - White Papers

Mouser Electronics White Paper Industrial organizations face steep costs and disruptions due to unplanned equipment downtime. According to a study by Plant Engineering, facilities lose an average of US$108,000 per hour during such outages. 1 In addition to financial losses, these interruptions undermine productivity and delay production schedules. At the same time, the aging industrial asset base is making maintenance increasingly challenging. Surveys have consistently identified legacy machinery as a leading cause of failure, with one industry study finding that 44 percent of unplanned outages were directly linked to aging equipment. 2 In response to these challenges, many organizations are turning to predictive maintenance (PdM) as a strategic solution. PdM is a proactive strategy that uses real-time industrial machine data and analytics to anticipate when equipment requires service. By continuously tracking these devices, maintenance teams can address issues before they escalate into unplanned downtime. The benefits of such a strategy are significant. A McKinsey report shows PdM reduces maintenance costs by 18 to 25 percent and increases equipment availability by 5 to 15 percent compared to conventional practices. 3 Preventing even one major outage enables PdM to pay for itself through higher uptime and throughput. This white paper examines factors affecting operational downtime and details the benefits and challenges associated with PdM systems, highlighting a suite of PdM solutions that are ready to enhance legacy infrastructure. Significance of the P-F Curve In reliability engineering, the Potential-Failure (P-F) curve illustrates the degradation of an asset from the first detectable sign of deterioration to functional failure, when it can no longer perform its intended function. As time progresses, the condition of an asset naturally declines due to wear, aging, and other stressors. The "failure begins" point (Figure 1) marks when a fault first appears, though it might not yet be noticeable. From that point onward, the condition worsens through several detectable warning signs like vibration, temperature rise, and noise, until catastrophic failure occurs. PdM approaches along the curve utilize real-time data—including vibration analysis, oil analysis, and temperature monitoring—to identify early signs of failure. PdM allows intervention at the first detectable change in asset health, which occurs much earlier than when audible noise or overheating becomes apparent. This extends the window between failure onset and catastrophic failure, giving maintenance engineers time to plan and execute maintenance without disruption. The P-F curve quantifies the lead time required for maintenance teams to address a problem after it is detected. An effective PdM system aims to maximize this lead time by using sensitive monitoring techniques to detect flaws as early as possible. In practice, PdM systems must use tools such as vibration analysis and thermal monitoring to reveal critical issues well in advance of failure. Requirements for an Effective PdM System To realize the benefits of PdM, a system must meet several key requirements: • Continuously monitor key health parameters of industrial equipment, including vibration levels, temperatures, pressures, and electrical signals. • Leverage machine learning algorithms and advanced signal processing to analyze trends and patterns in detecting anomalies that precede failures. • Promptly alert the maintenance team through visual or audible alarms and remote notifications about the identified issue so that action can be taken. • Use historical data that enables maintenance engineers to conduct more thorough root-cause analysis and optimize maintenance schedules. Figure 1: The P-F curve is used to determine asset reliability and performance. (Source: Banner Engineering; redrawn by Mouser Electronics)

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