This is Part 2 of a 5 part series on Smart Reliability IIoT Condition Monitoring; don't miss Part 1, Part 3, Part 4 and Part 5!
A commissioned Smart Reliability™ application provides an equipment hierarchy tree for equipment navigation which includes alert indicators at the tree branches and nodes. Navigating the hierarchy tree displays an individual mashup (webpage) for a specific equipment, and if any alerts are active, shows the alerting sensor and lists the probable failure modes developing within the equipment.
Smart Reliability provides several tools to setup and manage the predictive maintenance sensing technologies applied to the equipment. The first tool is the Failure Mode Analysis tree, which pulls failure mode analysis data from Allied’s database built on 25 years of failure mode analysis work on over 3 million assets. The tree allows navigation from equipment components, to component parts, to likely failure modes and failure reasons, and sensors that can detect defects leading to the specific failure mode. The Failure Mode tree indicates whether the specific sensor (ex. vibration) has been installed and provides visual cues and exportable listing of covered and uncovered failure modes as well as sensors that can be added to detect defects causing failure. Sensors include vibration, motor current and voltage, oil sensing, temperature, ultrasound, flow, and even operator inspections (QPM).
As sensors are added to the condition monitoring strategy, failure mode coverage and sensor coverage are updated. These statistics are useful in understanding condition monitoring coverage. However, some failure modes are more likely to occur than others. Some sensor types do a better job of detecting defects than others. For example, online monitoring using a traditional piezoelectric vibration sensor provides significantly better detection ability than a low-end overall vibration sensor based on MEMs technology. Smart Reliability adds a “Confidence Factor” which considers the likelihood of failure modes and the ability of the sensor to detect and provide detail of the defects that cause failure. In other words, are we covering the right failure modes, with sensors that do a good job detecting the contributing defects?
Smart Reliability delivers another visual cue for detectability of defects. This is the Point of Detection to Point of Failure or P-F curve. With the Smart Reliability P-F curve, we can visualize the timely-ness of defect detection. Can we detect defects months before the defect causes failure, weeks before, or just days and hours before? While we may have significant failure coverage, are we detecting defects early enough to properly plan and schedule the proactive maintenance work needed to remove the defect? The P-F curve helps us visualize where our applied sensors lie on the curve, giving us an indication of the ability of our sensors to predict failure.
Now that we have sensors (and potential operator observations) how do we set the alert levels for those sensors? Allied’s OP2 condition monitoring standards provide those alert settings based on 25 years of condition monitoring service best practice. The OP2 standards have alert and warning levels for each sensor type used in condition monitoring. The alert settings can be updated by Allied’s condition monitoring assistance program, or by client subject matter experts (SME). When sensor alert levels are exceeded, the likely failure modes at play are noted on the mashup noted at the beginning of this article.
Lastly, Smart Reliability provides short term trend charts for the sensor values it monitors, and a high-level equipment utilization page that comes to life when connected to the client’s CMMS data.
Smart Reliability takes the client team through the equipment maintenance plan development process using condition-based monitoring techniques. Graphical cues and tables enable the client or Allied’s team to select and deploy the appropriate sensor monitoring approach for our client’s important plant equipment.
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