In today’s ever-advancing digital age, organisations are continuously looking for innovative ways to optimise processes, enhance operational efficiency, and reduce costs. One area that’s gaining significant traction in the industry is the use of artificial intelligence (AI) in monitoring the health and performance of machines and equipment. Welcome to the era of predictive maintenance, where AI can foresee potential failures and optimize maintenance tasks in real-time.
What is Predictive Asset Maintenance?
Predictive maintenance is a proactive approach that leverages data analytics and AI to predict when equipment will fail or require maintenance. Instead of the traditional reactive approach or routine scheduled maintenance, predictive maintenance focuses on predicting and preventing equipment failures.
The AI Advantage
Real-Time Monitoring and Analysis:
Traditional methods involve periodic manual checks which might miss subtle variations or abnormalities. AI systems can continuously monitor machine conditions, analyse vast amounts of data in real-time, and detect even slight anomalies or changes that might indicate a problem.
Cost Savings:
Unexpected downtime is expensive. By identifying potential issues before they become major problems, companies can avoid the costs associated with unplanned outages, including lost productivity and emergency repair expenses.
Extended Equipment Life:
Regularly and accurately predicting and addressing maintenance needs can significantly extend the life of machinery and equipment, ensuring a better return on investment.
Improved Quality and Safety:
Predicting and addressing potential issues ensures that machinery operates at its optimal level, improving the overall quality of production. Additionally, avoiding unexpected machinery breakdowns can prevent accidents and improve workplace safety.
Efficient Resource Allocation:
With accurate predictions, maintenance teams can be better scheduled and prepared, ensuring they have the right resources and parts on hand when needed. This reduces wastage and increases efficiency.
How Does AI Achieve This?
- Data Collection: Using IoT sensors, the system gathers data from equipment in real-time. This data can include vibration, temperature, sound, and more.
- Senor Data Intelligence: Sensor Data Intelligence for D365 F&SCM Supply Chain Management will enable organisations to drive business processes in Supply Chain Management based on Internet of Things (IoT) signals from machines and equipment on the production floor. Sensor Data Intelligence lets you perform the following tasks:
- Collect details from machines and equipment to update maintenance asset counter values in Supply Chain Management and drive predictive maintenance.
- Set up the solution by using a simple onboarding wizard instead of manually installing and configuring components in Microsoft Dynamics Lifecycle Services (LCS).
- Deploy components on your own subscription so that you have more flexibility to manage Azure components.
- Configure, scale, and extend the solution as business logic that runs on Azure components. Those components are now managed on your own subscription. Therefore, you can customize them as required to meet your unique business needs.
- Data Processing: AI algorithms process this data, learning the normal operational parameters of the equipment.
- Anomaly Detection: Over time, the AI system can identify deviations from the norm, signalling potential issues before they escalate.
- Predictive Analysis: Using historical data, coupled with real-time data, AI can make predictions about when a machine is likely to fail or require maintenance.
AI & Maintenance Strategies:
AI technology can help enhance can help enhance & augment different types of Asset maintenance strategies such as the examples below:
- Corrective or Reactive Maintenance: Where maintenance is carried out following the detection of an anomaly and is aimed at restoring normal operating conditions
- Preventive maintenance: Where maintenance is carried out at predetermined intervals or according to prescribed criteria aimed at reducing the failure risk or performance degradation of the equipment.
- Risk-based maintenance: Where maintenance is carried out by integrating analysis, measurement and periodic test activities into standard preventive maintenance. The gathered information is viewed in the context of the environmental, operation and process condition of the equipment in the system. The aim is to perform the asset condition and risk assessment and define the appropriate maintenance program.
- Condition-based maintenance: Where maintenance is based on the equipment performance monitoring and the control of the corrective actions taken as a result. The actual equipment condition is continuously assessed by the on-line detection of significant working device parameters and their automatic comparison with average values and performance. Maintenance is carried out when certain indicators give the signal that the equipment is deteriorating, and the failure probability is increasing. In the long term, this strategy allows for drastically reducing the costs associated with maintenance, thereby minimising the occurrence of serious faults and optimising the available economic resources management.
Conclusion
The integration of AI in predictive asset manitenance is more than just a technological advancement; it’s a paradigm shift in how businesses approach machinery and equipment health. Companies that adopt this proactive approach not only save on costs but also position themselves as forward-thinking leaders in their respective industries.
At Intelisense IT, we’re committed to bringing the best of AI technology to help businesses thrive in this digital age. Reach out to us to explore how AI-powered predictive maintenance can elevate your operations to new heights.
