Condition Based Monitoring and
Condition-based maintenance (CBM) is a proactive maintenance strategy that detects when an asset's performance or condition has degraded to an unacceptable degree using real-time data (gathered through sensors). Maintenance work is only carried out as necessary in response to the asset's actual condition by keeping an eye on the asset's condition, avoiding pointless maintenance jobs.
Our Predict sensors and software make asset condition monitoring easy and affordable to implement.
How to Implement
A maintenance technique known as condition-based maintenance (CBM) involves continuously monitoring and checking an asset's condition to determine whether the repair is necessary.
Find the most important health metrics. Examine the asset's failure modes and history. Determine the variables that are reliable failure-leading indicators. As the asset deteriorates, these indicators follow a predictable path.
Make a baseline. The baseline against which performance must be benchmarked can then be established. A maintenance event is started by a departure from this, whether absolute or relative.
Continuous evaluation. Automatic or manual sensors are used to take continuous measurements. These are continuously compared to the performance or level that serves as the baseline and trended.
What impact you can anticipate?
Condition-based maintenance brings numerous advantages. Your plant can change thanks to condition-based maintenance, which enables you to strike a compromise between maintenance costs and reliability. It significantly reduces unplanned and scheduled downtime, extends the life of equipment, and boosts team productivity.
The time it takes to gather data and the difficulty in storing it are two of the key drawbacks of condition-based maintenance.
Widely used leading indicators for CBM
Electrical signature analysis
Non Destructive Testing (NDT)
Utilize our wireless and wired Internet of Things sensors and gateways to collect asset health data such as vibration, ultrasound, temperature, and magnetic flux.
Extract time- and frequency-domain characteristics (health indicators) from the sensor data. Set baselines and apply filters.
The baseline is regularly compared with trended health markers. When the baseline or threshold is crossed, alerts can be set.