How do you customize and adapt your predictive maintenance solutions for different equipment and scenarios?

In my experience, the challenge often lies in aligning predictive maintenance (PdM) goals with financial objectives. Maintenance departments are typically hesitant to set financial targets because they struggle to translate technical metrics into financial parameters. Consequently, most PdM investments are drawn from OPEX (maintenance budget) rather than CAPEX (investments). It's crucial to define technical indicators that carry financial significance and commit to cost savings or economic benefits to demonstrate a return on investment.

Effective PdM isn't one-size-fits-all. We customize solutions by first understanding your equipment's unique design and failure modes. We then select sensors that capture relevant data and train AI models on your specific historical data to predict anomalies. This tailored approach ensures alerts and maintenance actions target real risks, maximizing uptime and minimizing downtime costs.

PdM solutions should be tailored to the unique characteristics of each piece of equipment, considering factors like reliability, criticality, and operational context. By combining domain expertise, data science, and customization, we create effective predictive maintenance strategies that keep Plants running smoothly and minimize costly asset downtime!👷♂️💪😇

As the name suggests Predictive maintenance, it's important to collect all the equipment data. When we say data it means all the date historic date & current data that is going to help you while maintaining the equipment without wasting its life & time for the maintenance. Once you have the complete date, start analyzing the equipment condition & specifications. This way you can adapt & customize maintenance solutions.

From what I've seen, if there's no existing PdM program, the best approach is to start with visual inspections. Many maintenance managers are surprised to learn that visual inspections are part of predictive maintenance. This method is the most straightforward and basic yet often overlooked because it doesn't sell hardware or software. The next steps typically involve using portable data collection devices by qualified analysts and eventually progressing to online monitoring with fixed sensors and online systems.

Unlocking the treasure trove of data, facility leaders embark on a quest to assess and refine their data sources and quality. They: - Explore the vast landscape of available data, from sensor readings to operational logs, and historical archives to external feeds - Determine the relevance and value of each data type, aligning it with their PdM objectives and equipment-specific needs - Evaluate data quality through the lens of accuracy, completeness, consistency, and timeliness, identifying areas for improvement - Develop a data governance strategy to ensure standardization, security, and compliance - Validate data integrity through continuous monitoring and auditing, guaranteeing trustworthiness and reliability

Customizing and adapting PdM solutions includes the following:- defining clear objectives, assessing data quality, choosing appropriate methods and tools, implementing custom AI solutions, testing them, continuously monitoring and optimizing their performance. A single AI model or approach may not be universally applicable for PdM across different machinery, as each has its own distinct operational characteristics, failure modes, and data patterns. However, some transfer learning AI models may provide a good starting point, which can then be customized to address the unique aspects of each machinery. This approach ensures that PdM solutions are effective and tailored to specific machinery and operational scenarios.

Selecting the most appropriate monitoring techniques for each asset is crucial, whether it's vibration analysis, thermography, or ultrasound. In our Power-MI software, we catalog over 30 inspection techniques. It's essential to identify potential failures detectable with each technique for specific assets and determine the appropriate frequency of measurements, avoiding over-reliance on continuous online systems.

I often tell my students that the pilot project is part of the procurement process. This phase allows us to work alongside technology providers to validate our maintenance plan hypotheses, confirming that we can detect the failures we anticipate, achieve our technical objectives and (most importantly) verify the financial savings or benefits we initially projected.

Demonstrating the financial savings achieved through PdM is the best way to highlight the value of these initiatives within a company. Keeping detailed records of these savings facilitates further investments in PdM, enhancing the credibility of new investment proposals with thorough financial analysis.

Embracing a culture of continuous improvement, facility leaders refine their PdM strategies, harnessing the power of data-driven insights to propel operational excellence. By monitoring and optimizing PdM solutions, they: - Track key performance indicators (KPIs) such as downtime reduction, maintenance cost savings, and productivity gains - Conduct regular assessments to identify areas for enhancement and innovation - Update PdM objectives, data, methods, and tools to reflect evolving equipment and scenarios - Explore new technologies and techniques, like AI-powered predictive analytics or augmented reality-enhanced maintenance

The most urgent issue I've observed is the need to translate PdM into financial language. Once this is achieved, it becomes easier to secure funding for PdM investments from CAPEX (investments) instead of OPEX (maintenance budgets). Additionally, negotiating with suppliers should focus on achieving PdM objectives and metrics rather than merely purchasing equipment or technology..