Predictive maintenance, which uses data and analytics to estimate when equipment will fail, can provide businesses with an opportunity to address issues before they lead to downtime. Predictive maintenance normally goes beyond the traditional preventative maintenance approach which generally relies on pre-determined schedules or reacting to breakdowns.
This strategy takes real-time measurements of equipment and leverages analytics to learn about variations in scans, alarms, etc. Ultimately this allows the businesses to spot issues and potential issues earlier in the lifecycle of issues and (hopefully) to case failings before downtime occurs.
Artificial Intelligence (AI) essentially “supercharges” predictive maintenance by processing and analysing enormous volumes of data with the goal of making actionable predictions before equipment failings. AI can save time and money by operating across multiple industries including manufacturing, transportation, and energy.
What AI Can Contribute to Predictive Maintenance
AI enables predictive maintenance with the use of some key technologies:
- Machine Learning (ML): ML emphasizes a consistent data-driven investigative approach with the goal being the development of accurate allowances (estimates) of when derogatory failures may occur by identifying past, or present, behaviours, patterns, signals (context dependent indicators are heartbeat, vibration or temperature).
- Internet of Things (IoT): Sensors or actuators attached to equipment provide data of an operational state (i.e., pressure, noise, etc.) allowing AI to ingest data to interpret behaviour. 3. Data Analytics: AI accepts and interprets increasingly complex datasets in providing insight into thresholds of leading operational inputs when scheduling maintenance, or predicting when failures may arise (i.e., succeeding blocks of unusual noises could be the input signal of wear and tear). 4. Digital Twins: AI helps to create computerized models (digital twins) of actual equipment under scrutiny and allows its analysis to predict failure given environmental or operational variances (i.e., equipment use over a seasonal period).
5. Natural Language Processing (NLP): NLP the ability for AI to interpret language, has impact on identifying and grouping maintenance logs or technician notes in relation to predictive analysis given confounding nature of data sets.
The Process of AI-Powered Predictive Maintenance
AI is able to perform predictive maintenance with a seamless process:
- Collection: Sensors attached to machines collect a variety of metrics which include temperature, speed or vibration, often times in real time.
- Analysis: AI algorithms analyze this data and compare it against previous historical patterns to identify deviations.
- Prediction: The system identifies failures and, based on previous patterns, predicts when a part might fail.
- Scheduling: The AI will make recommendations for the best timeframe for the repairs to take place, allowing minimal disruption of the operations.
- Feedback: After repair, the data collected helps improve the predictive analysis with new datasets, refining the AI.
Applications Across Industries
AI-enabled predictive maintenance is being deployed at scale across industries:
- Manufacturing: AI tracks all machines on the assembly line to avoid interruptions in the production line (like catching a worn conveyor before the production stops).
- Aviation: Airlines use AI to predict engine faults which enhances safety and keeps airlines on schedule.
- Energy: in wind farms, AI analyzes multiple turbines and schedules the maintenance to avoid lost power generation due to failure.
- Transportation: Fleet companies use AI monitor the health of all vehicles to minimize breakdowns and increase lifespans of trucks.
- Healthcare: AI monitors the health of medical equipment to ensure everything is functioning properly for important procedures (like MRIs).
Advantages to AI in Predictive Maintenance
This method results in decreased expenditures, because it avoids unplanned downtime, which can cost factories millions of dollars per hour. Furthermore, extending the life of equipment by catching problems early, increasing safety by reducing failures, and therefore making more efficient use of resources to postpone impact from a repair solution. AI is also very accurate, which means there will be few manual labour or parts wasted.
Challenges to Management
While there are advantages to AI in predictive maintenance, there will also be challenges to be aware of. For example, good quality data is important to Para predictive maintenance. If sensor data is defective, then predictions will also be flawed.
Added to that, there are high costs to setup sensors and build an AI system, especially as a small business. If older equipment is used, they may require upgrades to use AI effectively. In addition, workers will need to be trained to access and utilize the systems and to rely on the predictive nature of the systems.
Looking Forward to AI for Maintenance
As AI continues to develop, predictive maintenance will become clearer. Improved ML will yield better results, and the long-term costs of sensors and AI tech will decrease. The integration of augmented technology could deliver real-time help for technicians during obsolescence. Moreover, benefits will be seen in a higher number of IoT devices, allowing for more expansive real-time monitoring capabilities.
Conclusion
AI is developing predictive maintenance by converting information into actionable value, allowing businesses to eliminate costly downtime and run effectively. This technology shifts the predictive maintenance paradigm from post-breakdown inspection to avoiding the breakdown or problem altogether.
From a factory to an airline, the ability to predict a potential problem before it causes a major issue is impressive and transformative. Although challenges still exist, including the data and the costs, the advances in the technology will make it more efficient and cost-effective. The trend will be that equipment breakdowns become infrequent and manageable when they do occur.
