Manufacturers in both the process and discrete manufacturing industries should consider condition monitoring (CM) for critical assets to improve the effectiveness of their maintenance resources. Typical benefits include improved uptime, asset longevity, cost control, yield/quality and safety. Now is the time for users to re-evaluate their condition monitoring strategies and consider taking advantage of available packaged predictive maintenance (PdM) applications to leverage their existing process and smart device data.
Condition Monitoring Approaches and Benefits
Currently, maintenance strategies are generally classified into four types: reactive, preventative, predictive, and proactive. The included examples have intentionally been kept basic and generic.
Reactive – Run to Failure
Reactive (run to failure) is the most common approach to equipment maintenance. Since the majority of assets have a very low probability of failure and are non-critical, this is appropriate in many cases and helps control maintenance costs. However, when a failure does occur, the broken component can cascade into other components and become a major expense. Much like not servicing the engine oil in your car, running to failure, and having a $5,000 engine replacement because the bearings seized rather than a $50 oil change. This approach is appropriate for only non-critical assets.
Manufacturers often employ a preventive maintenance approach. Here, maintenance is performed based either on time (replacing the batteries in your household smoke detectors once a year), or usage (changing your car's oil every 5,000 miles). Preventive maintenance fits when failure is driven by wear with age, run time, or number of cycles.
Run to failure and then repair
Failure is unlikely, easily fixed, or non-critical
10X plus when failure occurs
Service in a fixed time or cycle interval
Maintain based on calendar or operating time
2X maintenance costs
Predictive (Condition Monitoring)
Monitor process data to identify bad trends and create an alert prior to failure
Identify when a failure is likely, and schedule repair when needed
1X maintenance costs
Proactive (Analytics & Small Data)
Equipment specific data acquisition, and algorithms, analytics, and/or a model
Longer range prediction of failure with high confidence
Lower engineering costs per unit
Equipment Maintenance Strategies
PdM using condition monitoring predicts when something bad is about to happen early enough so appropriate maintenance can be performed prior to failure. The applications involve the more critical assets where failure significantly impacts uptime, asset longevity, safety, product quality, or involve major repair costs.
One approach to PdM is to assess the current condition of the equipment using process or device data. This data often comes from the plant historian, and it is used with an algorithm for predicting failure. Another approach for PdM involves a separate plant asset management (PAM) system for condition monitoring. Commonly used sensors include vibration, infrared, ultrasonic, oil analysis, and corrosion. PdM systems are usually applied to rotating equipment and other machinery including pumps, electric motors, fans, internal combustion engines, and presses. Periodic inspections and condition evaluation are often used for stationary plant equipment such as steam boilers, piping and heat exchangers.
"Small data" comes from a particular device. Proactive maintenance combines the small data with algorithms that model that type of equipment (virtual equipment) to monitor condition and raise an appropriate alert. The small data from a particular device, combined with algorithms designed for that type of equipment, provide a means to assess condition and identify a problem before it cascades into a much larger impact on business performance. While "big data" has been getting a lot of attention lately, small data can provide a granular approach for generating viable alerts in industrial plants and factories when applied to real-time operational issues like condition monitoring.
One benefit of the virtual equipment is the ability to replicate it – like a template – across many similar devices in a large network – like pumps in a refinery or transformers in power transmission lines. Another major benefit of using a model comes from longer time horizons for notification of a pending issue. Integrating the alerts into other applications and business process automation (BPA) becomes important. Humans tend to forget long-term issues. "BPA–like" integration with the EAM system often assures resolution prior to failure.
Benefits for PdM and Proactive Maintenance
Maintenance Cost Control
Yield or Quality
Business Drivers for EAM/FSM Systems
As one moves along the learning curve from run to failure and towards preventive, predictive, and proactive maintenance; improvements occur in the core key performance indicators (KPI) for asset management and maintenance. They are listed in rank order based on the results of multiple surveys performed by ARC Advisory Group: 1) uptime, 2) asset longevity, 3) cost control, 4) yield/quality, and 5) safety. Note that some industries, such as refining, rank safety higher. These KPIs relate directly to executive metrics for the C-suite – hence their importance. The reported specific benefits for PdM over preventive maintenance include:
- Maintenance costs reduced by 50 percent
- Unexpected failures reduced by 55 percent
- Mean Time Between Failures (MTBF) increased by 30 percent
- Machinery availability increased by 30 percent
Another key consideration involves the aging workforce and the difficulty hiring replacements willing to work in an industrial setting. Some have forecasted double-digit reductions in the available workforce for industrial companies in developed countries. With the combination of fewer people and continually aging assets, more effective maintenance practices are required. Doing maintenance when conditions warrant (PdM) rather than periodically (preventive) requires less labor and provides a means to mitigate the aging workforce challenge.
Sustainable Condition Monitoring
Using process data to alarm and drive maintenance work (like the oil pressure light on a car's dashboard) is a well-recognized concept. PdM has proven to reduce maintenance costs while improving uptime.
Workflow: Avantis Condition Manager Layers on the Historian for PdM
Low PdM Adoption
Unfortunately, while condition-monitoring has high recognition, its adoption level is low. ARC's user surveys underscore this low current adoption rate of condition monitoring in industrial plants. Only 35 percent of plants have deployed condition monitoring and those are reported to have a low number of instances. Due to the diverse nature of sensing devices and related software technologies, a high degree of expertise and customization is often required to successfully implement a PdM solution. After implementation, lack of support leads to a high decay rate. The shortage of engineering resources to implement and support custom PdM applications results in a very small portion of the critical assets having condition monitoring.
Rather than unstable custom projects for PdM, a packaged application can often provide a more viable approach. An example of this type of software is Schneider Electrics' Avantis Condition Manager. It provides a wizard approach to select process data, configure diagnostic algorithms (formula or Boolean logic), and create alerts that drive the appropriate actions by maintenance or operations. The process data typically come from the plant historian and can include other systems compatible with OPC communications. The alerts provide a means to integrate with other systems for business process automation. For example, an alert can be passed on to the EAM system, and automatically create a work order for the planner to approve and schedule.
Advanced Pattern Recognition
An emerging application of technology for PdM involves Advanced Pattern-Recognition (APR). This technology generates an empirical model by "learning" from an asset's unique operating history during various stable and dynamic process conditions. This model becomes the baseline profile for normal operations for a specific piece of installed equipment or system. The APR system automatically compares an asset's model with real-time operating data to detect subtle changes. These changes provide early warning signs of impending equipment failure before they reach alarm levels and possibly an unplanned shut-down.
Though APR can operate with existing sensors, the Industrial Internet of Things (IIoT) offers a richer set of process data for a higher fidelity model with improved condition monitoring and asset reliability. The combination of these two emerging trends - IIoT and APR - offers an opportunity to take proactive maintenance to a new level.
Schneider Electric Avantis APM Solution
As ARC learned in a briefing, the Schneider Electric Asset Performance Management solutions include both Avantis EAM and Avantis Condition Manager. According to the company, these software packages are designed to help industrial customers improve the availability and performance of their critical assets and support real-time maintenance execution. The Avantis EAM offering has a range of functions for managing maintenance including asset information, work order planning and scheduling, purchasing, inventory management, human resources, and financials. Avantis Condition Manager collects operational information and performs diagnostics in real time. It is platform-independent, enabling it to work with a variety of EAM and control systems. This product has matured with over 10 years of use, customer feedback, and development.
User Interface for Configuring PdM with Avantis Condition Manager
Owner-operators should review their maintenance strategy and consider more significant adoption of condition monitoring and predictive maintenance solutions – particularly with the availability of packaged solutions that can help significantly reduce engineering implementation and support costs. The primary benefit comes with improved KPIs for uptime, asset longevity, cost control, yield/quality, and safety. A study by a major petroleum company showed that, compared to calendar-based preventive maintenance, predictive reduces maintenance costs by 50 percent. Also, improved maintenance effectiveness mitigates the lack of qualified candidates to replace the aging workforce as they retire.
With the growing influence of the Industrial Internet of Things (IIoT), this application space will continue to grow in adoption with broader opportunity for optimization. Expect small data and virtual equipment models to take performance to a new level in the near future.
All signed-in ARC Advisory Group clients can view this report in pdf format at this Link
If you would like to buy this report or obtain information about how to become a client, please Request ARC Info
Keywords: Condition Monitoring, Predictive Maintenance, Proactive Maintenance.