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Industrial maintenance plays a vital role in sustaining reliable equipment performance, safety, energy efficiency, regulatory compliance, and warranty protection. Maintenance managers are expected to ensure all factory assets are in peak operating condition. There are three different types of maintenance strategies applicable in a factory, including corrective, preventive, and predictive maintenance. Amongst these, predictive maintenance has been recognized among the top use cases in smarter factory and connected product verticals.
The global predictive maintenance market size accounted for USD 4.5 billion in 2022 and is expected to grow at a CAGR of approximately 27% to reach USD 49.34 billion by 2032. (Source: Precedence Research)
Selecting the right maintenance technique is important. Organizations can either opt for reactive maintenance (maintenance & cost of asset failure is low hence, allowed to run to failure), preventive maintenance (maintenance cost is low but cost of asset failure is high hence, analytics helps reduce costs further), or condition-based/predictive maintenance (maintenance and asset failure costs are high and predictions are complex).
Predictive maintenance solutions use a proactive approach with real-time data to analyze the condition of equipment using machine learning techniques to identify equipment vulnerabilities or anomalies. The objective is to schedule maintenance at the most convenient and most cost-efficient time, allowing the equipment’s lifespan to be optimized to its fullest, but before the equipment has been compromised.
The pre-requisites for predictive maintenance include steps such as assets identification, data collection, analysis, archival and performance baselines, etc.
A successful predictive maintenance journey generally consists of these three phases:
Phase 1: Anomaly detection that includes selecting the anomaly model(s) through machine learning, generative AI algorithms, and human validation.
Phase 2: Condition-based maintenance (CBM) that aims to predict the upcoming potential failure by identifying patterns and trends in asset data such as maintenance logs, alarm logs and so on.
Phase 3: Predictive maintenance that involves putting predictive models into practice that can forecast extremely accurate metrics like lifespan, time to failure, and real usable time (RUT), as well as suggest the primary reason for failure.
Conclusion
The impact of predictive maintenance extends far beyond cost savings, encompassing increased equipment lifespan, enhanced safety, and a significant boost in productivity. By harnessing the power of data, IoT, and advanced analytics, companies can transition from reactive to proactive maintenance, ultimately ensuring a sustainable, efficient, and safe industrial future and gaining a competitive edge.
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