A Vision for the Next DecadeAs we look toward the future, the field of prescriptive maintenance is poised for a transformative leap. The convergence of advanced technologies, including AI, edge computing, and IoT, is set to redefine the way we approach machine health and maintenance. Here are eight key directions that will shape the future of prescriptive maintenance:
1. Expert-level AI
The future of prescriptive maintenance will be driven by autonomous, expert-level AI systems. These systems will be trained on massive proprietary datasets, enriched with expert feedback and failure data. The goal is to create AI systems that can match, and even surpass, the expertise of a Category IV vibration analyst. This level of expertise can only be achieved with large datasets labeled by expert-level vibration analysts and with the AI continuous learning from customer repairs and inspections.
2. Edge Computing
By processing data at the edge of the network, close to the source of data generation, it will be possible to achieve highly frequent sampling and to deliver data to the end user within seconds to minutes. This can in turn get ahead of rapid failures and cover machines with all types of duty cycles or intermittency.
3. Real-Time Alerts & Continuous Insights
The combination of expert-level AI and edge computing will enable real-time alerts and continuous insights. This will provide customers with even earlier warning, especially for critical rapid failures. In-house expert vibration analysts will continue to play a vital role, providing additional consultation, feeding the AI with important metadata and context, and dealing with complex coverage cases that the AI is still learning to handle.
4. Personalized AI
The future of prescriptive maintenance will be personalized. AI systems will adapt to specific failure patterns, false alarms, and customer preferences. AI will not only learn from your data and expert labels, but also your history of inputs to the system on common failures modes, successful routines for repairing various faults, routine operational changes, and your desired level of alert sensitivity. This level of personalization will improve diagnostic quality and create a symbiotic relationship where the AI works for the maintenance professional and they improve operations together.
5. Breaking Ground on New Coverage Problems
Already, we are seeing how bespoke sensing and fine-tuned AI can break ground on new coverage problems. This will allow AI, Edge Computing & IoT-driven systems to cover a wider range of machines and failure modes, further enhancing the effectiveness of prescriptive maintenance.
6. Platform Integrations
The future of prescriptive maintenance will involve deep integrations between platforms and specialized solutions. On the one hand, we will see integrations with sensing systems, such as Circuit Signature Analysis (MCSA), Ultrasonic, and Thermography. These integrations will allow for covering more and more machine failure modes, such as electrical, lubrication and the extreme rotational speeds.
On the other hand, integration with OT and IT platforms, such as Computerized Maintenance Management Systems (CMMS), and Parts on Demand, will allow for automating processes as machine and process health systems identify problems and recommend steps for improvement.
7. Integration with Process Data
The integration of machine health data with process data will further allow for covering more types of assets. What’s more, it will open up opportunities to optimize production. By leveraging various types of data, including ultrasonic testing (UT), voltage, current, pressure, and flow, we can gain a more comprehensive understanding of machine health and performance. This integration behind a “single pane of glass” will enable us to optimize both maintenance and production outputs, leading to improved operational efficiency, productivity, and yield.
8. Affordability
As AI becomes more automated and sensor technology becomes cheaper, prescriptive maintenance will become more affordable. However, achieving this scale will require significant upfront investment in data collection, AI training, and IoT fleet management.
In conclusion, the future of prescriptive maintenance is exciting and full of potential. By embracing these future directions, we can create a more accurate, scalable, and comprehensive approach to machine health. This will not only improve the efficiency and effectiveness of maintenance strategies but also contribute to the overall productivity and profitability of businesses.
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