As process industries expand their presence around the globe, it is increasingly important to track the condition of connected assets across different sites in real time and from one central location. Furthermore, the need to prioritise asset renewals and capture process knowledge becomes even greater as infrastructures and workforces age. The fundamental questions now being asked are “Can I avoid unplanned maintenance in my plant?” and “If failure is inevitable, how do I get to know well in advance of its likelihood so that I can prepare?”.
Who is affected?
Plant asset management systems typically have two different user types. First, there is the site or plant owner, who has an active interest in tracking the overall status of all assets through real-time condition monitoring. Within the plant there are process engineers interested in the details of each component and the likelihood of it provoking a bottleneck or the reasons why it is underperforming and causing production to run at lower capacity. At the same time, multi-national companies with enterprises around the world are keen to compare the performance of assets across all sites. In this case, it is critical to be able to display the information in dashboards, in order to see at a glance how a process is running or equipment is performing.
The second user type is responsible for entire fleets. Fleet owners may not be interested in the plant’s day-to-day capacity performance. Instead, they may be subject matter experts (SMEs) who specialise in equipment such as compressors. By monitoring a compressor fleet across several sites, SMEs can then compare performance data, perform root cause analyses and track any weak links.
For both these user groups the benefits of plant asset management include:
- Reduced time for anomaly detection and root cause analysis
- Increased asset utilisation and uptime
- Reduced maintenance costs by shifting to predictive maintenance
- Mobile access to asset performance information
- Fleet enterprise view and analytics of asset performance
What is affected?
Among the assets best placed for real-time condition monitoring are process automation systems, connected devices and field devices such as sensors and actuators. This includes basic instrumentation equipment for all processing and manufacturing industries, motors, transformers, circuit breakers, complex electrical or rotating equipment and mechanical handling equipment such as conveyor belts.
Predictive maintenance is the ability to determine exactly how an asset is performing by identifying, diagnosing and prioritising imminent equipment problems – not just locally but at an enterprise-wide global level. In the event of a problem, users can make informed and quick decisions based on clear recommendations. The possible actions range from immediate attention to identifying issues that can be resolved during routine maintenance. This helps companies reduce unscheduled downtime, prevent equipment failures, extend their asset’s lifecycle and make sure that the installed base is operating and maintained optimally.
Traditionally, predictive maintenance will be carried out by SMEs with years of experience understanding the characteristics of critical equipment such as compressors, pumps or motors. Software tools already available today try to capture the experience of such experts and apply them programmatically to diagnostic data.
For example, the performance of a heat exchanger can be continuously monitored and alerts generated when that performance starts to degrade beyond a certain point. The operator can then decide whether to continue running the heat exchanger, schedule maintenance or stop the process immediately for instant repair. Costly failures are avoided, or else the operator takes a conscious decision to continue with a calculated risk.
Future technologies, such as machine learning (ML) and artificial intelligence (AI), are set to offer more advanced analytics that will make predictive maintenance decisions quicker. Applying ML and AI to huge chunks of diagnostic data can automatically cleanse it, detect anomalies and highlight them for the SME. Thus, the SME can focus on these anomalies and rapidly determine why performance may be slipping.
Flexibility in deployment
With confidence growing in cloud technology, it is only a matter of time before users move away from on-premise solutions. In the interim, some automation vendors are offering Edge computing devices for those users preferring on-premise software. Edge technology acts as a platform for applications to give customers the option of operating solely on the premises rather than in the cloud.
For example, ABB Ability Edge secures the connection between the cloud, control systems and smart devices, efficiently separating information technology (IT) from operational technology (OT). Data from Edge can be utilised for plotting, charting, trending and dashboards. Alternatively, it can be used by experts in data analytics or connected to ERP and maintenance systems. The extensive connectivity capabilities allow Edge to ingest data from countless devices and systems and make it available for cloud applications.
Flexibility in deployment and a consistent architecture across the cloud and Edge allow customers to use the same asset condition model, either in the cloud or near the source using Edge. Flexibility in connectivity is likewise important. As well as the control system, Edge provides the ability to connect to many other assets, including the numerous IoT devices that are meanwhile being deployed in non-mission critical monitoring.
While often referred to as low cost sensing, these IoT devices can make a substantial contribution to understanding an asset’s performance. The biggest benefit of using the cloud is the computation power for overall analyses on a fleet/enterprise level together with the ability to share data instantly between different teams across the enterprise.
The consistent architecture across Edge and the cloud enables seamless OT/IT integration as well as effective asset management, from individual devices to the entire fleet.
Asset Management Application
ABB is taking a lead with the launch later this year of its Asset Management Application, a comprehensive library of asset models including a tool for customisation and advanced analytics. This web-based application provides fleet and enterprise dashboards of asset status. The Asset Management Application allows quick comparison and benchmarking of key performance indicators for running process equipment across different sites. It helps customers achieve higher levels of productivity and improve economic return to meet their performance and quality objectives. The application avoids costly failures in the process industry by managing and protecting assets through predictive and preventive maintenance. By optimising workflows and providing in-depth analyses of data, asset management strategies are more cost effective and feature better decision-making.
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