“It is not rocket science, we’re just automating the mundane tasks.”
The Evolution of Predictive and Condition Based Maintenance over the last 40 Years
By Brig YVR Vijay
Introduction
1. Adding condition monitoring and predictive maintenance for legacy equipment is touted today as a significant source of return on investment (ROI). For some, predictive maintenance is a new term, one many confuse with condition monitoring.
2. The main difference in predictive maintenance and condition monitoring is the timing. While both monitor the health and condition of an rotating or vibrating mechanical assembly asset, condition monitoring focuses on here-and-now conditions. Predictive maintenance focuses on the early detection of defects, 60 or 90 days before the defect causes collateral damage or impacts production. The final step to realize success in any predictive maintenance program is to translate the alert or condition to a specific or “prescriptive” maintenance task.
Condition Monitoring in the Pre-Sensor Era
3. Much like the red alerts on the dashboard of your car, legacy condition monitoring in MRO industry has included lagging indicators such as:
(a) Low lube oil pressure
(b) High temperature
(c) Low or high discharge pressure
(d) Low or high seal pressure
(e) Low or high seal potential level
(f) Abnormal Sounds
4. An alert condition on these measurements means a failure has already or is currently taking place and a timely response is required. In predictive maintenance, this is referred to as condition-based-reactive maintenance. Although useful, the indicator does not give enough time to strategize. Production does not have enough time to plan and maintenance does not have enough time to line up the right parts or skills. Still it is very useful for MRO. In almost 90% of the MRO industry this is all that is being followed.
Condition Monitoring Development in the 1990s
5. A second wave of measurements was adopted by industry and has dramatically improved the detection of defects. This is basically sensors to detect current, speed, power, vibration frequencies and temperatures. Easy mounting of sensors on the drive train and in bearings enabled this. This second wave of measurements includes motor current, speed, power, overall vibration and bearing temperature. A variance in any of these measurements can indicate a condition that needs attention. Using these measurements has proved fruitful for diagnosing problems.
6. Overall vibration deployed with knowledge of alert standards has helped identify pre-existing conditions. Clarification of the failure modes detected by overall vibration has helped explain misses. Failure modes detected by overall vibration include imbalance, misalignment, looseness and late-stage bearing or drive train failure. Overall vibration is a direct measurement for detecting and monitoring imbalance, misalignment and looseness of rotating assets. Overall vibration is typically calculated from an acceleration reading measured using a accelerometer. A variance from OEM standards is what one looks for.
7. While overall vibration is excellent at detecting the presence and severity of imbalance, misalignment and looseness, many would argue it is not predictive and that overall vibration is a lagging indicator, as the problem or defect already exists. Yet, finding an imbalance or looseness defect when it is small has significant benefit if operations and maintenance have enough time to take the machine down to fix the problem while it is still small. It is best to repair the problem early, when it is a small cost, in comparison to waiting too long and fixing the problem when it has caused additional collateral damage. We as EME can easily upgrade our legacy equipment with these sensors and periodically use hand held meter readings to incorporate some level of predictive/condition based maintenance in our older equipment in the Army.
Condition Monitoring / Predictive Maintenance of the 2000s
8. IIoT measurements for predictive maintenance is much akin to the discussion around business key performance indicators (KPI)-leading versus lagging. The monitoring described earlier, although good, is still lagging or condition based. For some, predictive maintenance is synonymous with technologies such as:
(a) Infrared thermography (IR)
(b) Ultrasonic
(c) Discharge testing
(d) Analysis of vibration spectrum
9. With IIoT more intelligent sensors, coupled with more intelligent processing, communication, storage, alerting and translating, has emerged. The failure modes targeted by this new intelligence includes 60- and 90-day advance detection of lubrication defects, bearing defects, cavitation and seal failure. Multiple equipment studies agree lubrication is the root cause of failure on 50 to 80 percent of rotating/moving assets. Past technologies of overall vibration or bearing temperature were simply too late or too difficult to establish meaningful alerts. In Condition Monitoring now overall vibration in combination with high frequency or ultrasonic provides the opportunity to realize predictive maintenance, where a fault condition is identified 60 or 90 days in advance. This allows operations and maintenance to plan and schedule a repair, with the right parts, tools and skills at the right time.
10. Ultrasonic vibration [1,000 to 25,000 hertz (Hz)] is the measurement for detecting lubrication, bearing fault, gearbox defects and cavitation. The units for high-frequency vibration or contact-based ultrasonic is Gs (units of gravity), which is an acceleration measurement. Ultrasonic is a less-known measurement and does not have any ISO standards. The one requirement of an ultrasonic measurement that is common across all sensors is for the sample rate to be quite high say equal to or greater than a few ten thousand samples per second. Thankfully, in recent years the processing power of computer chips for this demanding processing has become affordable and readily available. Also they are quite temperature resistant. This is an area where huge earnings can be realised by developing ultrasonic testing equipment and using them on equipment.
Condition Monitoring Using Industrial Artificial Intelligence
11. The current advancement in predictive maintenance is to further automate the analysis process using artificial intelligence (AI) models. With the new and rich data stream from ultrasonic sensors, edge processing of the data and connectivity to the cloud, proven AI models have been developed that detect pre-existing conditions even earlier. Established AI models allow 90 percent or more of industrial assets to have alerts accurately established upon initial deployment. It also can amass the leading indicators mentioned above with sensors and connectivity, but also make the leap to automatic pattern detection driving quantitative patterns with various weak indicators or the specifics of the bearing/drive train itself.
12. The final step to realize success in any predictive maintenance program is to translate the alert or condition to a specific or “prescriptive” maintenance task. For this application, a second AI model is deployed that translates the alert or machine learning classification and severity to a prioritized maintenance task, which is then emailed or integrated with existing computerized management systems for planning, scheduling and managing maintenance work orders.
This frees human resources for higher-level thinking. The goal is to focus on the right work or assets and do fewer things with a higher standard by examining indicators at high frequency.
