Welcome to the second post in this series. In the first post, we introduced the five processes of maintenance work order management and started to review those processes in detail and focused on work identification and what makes a great work order. In this post, we’ll look at how work identification in big industry has traditionally been managed, and how technology is revolutionizing efficiency.
Whether a work order is preventive or corrective, it will be generated based on one of a few triggers or activities. For the purposes of this post, we’ll consider traditional management of work that is triggered via condition based monitoring (CBM), predictive computerized maintenance management system (CMMS)-based activities, visual identification, and regular, time-based maintenance tasks. Then we’ll look at how technology is changing and impacting the status quo.
Condition-based monitoring (CBM) work order trigger
Condition-based monitoring is a predictive maintenance strategy. It utilizes sensor data from gauges and IoT, to vibration analysis and alarms indicating variable pressure, temperature, flow changes and other deltas to monitor the condition of assets in real-time. This data can help increase throughput and asset utilization by reducing unscheduled downtime and maintenance costs while driving productivity improvements and cost savings.
CBM: Current state and challenges
Sensor data is captured in disconnected systems, real time information is frequently only available in 2D, and alerts and alarms are on equipment or in control rooms.
Data is siloed, requiring significant time to locate, aggregate, analyze and share through yet further disconnected channels. 2D data (the standard systems of record within the company, extending into file and object storage and application and insights via warehousing and data lakes) is difficult to search and access. There is no way to compare assemblies, parts or objects at the 3D, geometric level, and frequently it requires a 3D view to determine if highly similar parts with different file extensions due to different CAD packages (for example) are in fact identical, or different. Alarms and alerts require on-site monitoring and checking in control rooms or equipment.
CBM: Optimizing the current state with technology
Digital twin technology is making significant inroads with regards to safety improvements, productivity improvements and cost savings. This technology ingests, aggregates, unifies, and analyzes all engineering, geospatial, planning, scheduling and operational data including sponsor data in real time (or near real time) in a single pane of glass and in the context of a reality site.
Your entire team and third-party contractors are able to remotely access, collaborate on, and view the same asset data in the context of both 2D and 3D modeling. Subject matter experts can add further context to visual data sets to help decision makers make better informed decisions, and non-subject matter experts are able to analyze the latest data within the 3D viewer from anywhere in the world with an internet connection.
Predictive CMMS based work order trigger
Maintenance tasks that possess the following three attributes are considered predictive maintenance; (1) they are systematic, (2) they are performed routinely, and (3) they are aimed at minimizing or reducing failures.
Current state and challenges in predictive maintenance
Predictive maintenance is scheduled and is aimed at optimizing capital asset life. It includes repair, replacement, adjustments, lubrication and cleaning. Current workflows are disconnected and rely on textual information, making it difficult to understand exactly what is happening on site. As remote sites are difficult to travel to, maintenance teams need to rely on historical data, manufacturing specifications, on-site sensors, and weather patterns to predict when maintenance is required. Lots of assumptions are made in this workflow, resulting in inefficiencies and risks in the organization. Having a visual tool to confirm existing site conditions provides maintenance teams with additional context to predict and plan maintenance activities more effectively.
Optimizing predictive maintenance workflows with technology
With reduced barriers to entry to reality capture technology, organizations are finding new value in capturing and visualizing their remote facilities. Deploying scheduled mobile mapping scanning on large industrial assets, and drone scanning on long, linear assets provides powerful insights to the asset management team without needing to send anyone else to site.
VEERUM's digital twin software provides a collaborative tool enabling visual inspections of remote facilities. Teams are able to input images to geospatial locations to identify defects, and 360’ panos help visualize current site conditions to better support inspection. Visual inspections allow off-site stakeholders to analyze the digital twin and identify any issues on site. Hosting all this information in the context of a collaborative 3D viewer enables remote planning and orientation, so when stakeholders need to perform on-site maintenance, users are familiar with site conditions before they arrive, reducing the need for unpredictable and risky rework. Adopting these new technologies today will continue to promote the use of collaborative digital tools across the industry, and in turn, assist in training AI/ML algorithms to enhance predictive maintenance practices over time.
In our next post we’ll continue with the current and future state of work order triggers finishing with visual identification, and regular, time-based maintenance tasks.
Interested in learning more about digital transformation in operations and maintenance? Download the full white paper on solving the biggest maintenance challenges in the energy and mining industries here.