Data from preventative and predictive maintenance can provide valuable insights into the condition and performance of machinery. Here are some key types of data collected by AIM from each approach and what they can reveal:
Preventative Maintenance Data
1. Maintenance Logs: Records of all scheduled maintenance activities performed, including dates, tasks completed, and parts replaced. This helps track the history of maintenance and can inform future scheduling.
2. Frequency of Issues: Data on how often specific issues arise can indicate the reliability of equipment and help in adjusting maintenance schedules.
3. Replacement Cycles: Information on how long components last before they need replacing can inform inventory management and help optimise stock levels.
4. Work Orders: Detailed work orders can provide insights into common repairs, helping to identify trends and areas that may require more rigorous monitoring.
5. Cost Tracking: Analysis of maintenance costs over time can help assess the economic impact of various maintenance strategies and inform budgeting decisions.
Predictive Maintenance Data
1. Sensor Readings: Continuous data collected from sensors monitoring parameters such as vibration, temperature, pressure, and sound levels. Anomalies in these readings can signal potential failures.
2. Historical Performance Data: Analysis of past performance data can help predict when maintenance will be required based on trends observed over time.
3. Failure Modes: Data on types of failures that have occurred historically, which can help in understanding weak points in machinery and focus predictive efforts on those areas.
4. Remaining Useful Life (RUL): Predictions based on the current condition and performance metrics that estimate how much longer a piece of equipment can operate effectively before needing maintenance or replacement.
Potential Insights Gained
1. Condition Assessment
2. Optimised Maintenance Scheduling
3. Increased Safety
4. Cost Management
5. Enhanced Decision-Making
By utilising both preventative and predictive maintenance data effectively, organisations can ensure that their machinery operates at peak efficiency and reliability, ultimately contributing to improved productivity and competitiveness in the market. This is a maintenance strategy that involves the continuous assessment of the real-time condition of equipment and machinery.
