Field instrumentation and analytical chemistry device suppliers have made tremendous progress over the last decade incorporating value added functionality into intelligent devices. This includes enhanced visualisation and health monitoring functionality to facilitate predictive maintenance (PdM). Despite these technological advancements, many manufacturers are not utilising digital device diagnostics to their best advantage. Consequently, plant operational efficiency has not improved significantly nor have costs due to device related accidents decreased.
To address this issue, the International Society on Automation (ISA) has recently formed a new standard committee, ISA108, to characterise intelligent device management in the process industries. The committee will define standard templates for best practices and work processes based on information derived from intelligent field devices, including models and terminology, implementation guidelines, and detailed work processes.
Plant asset management system landscape
ARC defines plant asset management (PAM) systems as hardware, software and services that assess plant asset health by monitoring asset condition periodically or in real time to identify potential problems before these can affect the process or escalate to a catastrophic failure. Asset monitoring, one set of applications falling under the asset performance management umbrella, also includes enterprise asset management (EAM), mobility, reliability, enterprise resource planning (ERP) systems, and other sources of information. These include energy management systems (EMS), environmental, health, and safety (EH&S) systems, and sustainability.
Asset performance management (APM) systems provide a compelling case for reducing operational costs while simultaneously improving operational performance. APM leverages the power embedded in various operations and maintenance applications to improve asset availability and utilisation within the collective operational constraints of the enterprise. However, to date, the emphasis has been on monitoring production assets. ARC research indicates approximately 75% of monitoring investments target production assets. As illustrated above, most production assets contain moving parts that are subject to wear and degradation. Vibration technology is used extensively to monitor these assets.
The evidence indicates that automation assets are taking a backseat when it comes to monitoring asset health. According to Ian Verhappen, co-chair of ISA-108, “More than 80% of smart instrument data is not being used or even connected to an online data collection system.” ARC believes that this is counter intuitive given that production asset monitoring frequently requires additional external equipment, while most automation assets already contain a high degree of embedded intelligence. While the level of digital technology implemented in field devices is evolving, particularly in wireless transmitters, operational enhancements will not be realised if organisations continue to underutilise the available functionality or employ old work practices.
ISA-108 intelligence device management
Given that intelligent devices and products are widely available, there is a consensus that end users are not utilising the available device diagnostics. Traditional maintenance work processes often exacerbate this situation. Poorly defined problems, for example, waste time and effort. Maintenance for non-critical devices is frequently deferred. Scheduled inspections and testing that reveal nothing are necessary, but wasteful.
Formed in August 2012, the ISA-108 committee is charged with defining standard templates of best practices and work processes for the design, development, installation and use of diagnostic and other information provided by intelligent field devices in the process industries. The belief is that when intelligent devices are properly utilised and managed, maintenance staff can focus on the devices that actually need work when the data indicate attention is required. Devices can provide detailed information on problems before a trip to the field is made. This could result in significant reductions or elimination of periodic testing and provide advanced warning of failures to reduce their impact on operations.
The scope of the committee work products will include recommended work processes and implementation practices for systems that utilise information from intelligent field devices and the people who use them. Work process templates by worker roles (such as maintenance or operations) will be one area of research. The committee will develop both best practices for implementation and models for the flow of information from devices through the various systems that use the information.
Because no new technology is involved, the primary focus of the committee will be on developing new work processes to match device capabilities. This will require a cultural change, which can be the most challenging to implement. The committee will also target alarm management and rationalisation as it advocates for a risk-based approach to alarms to alleviate fatigue. Intelligent device data can make the distinction between operator or maintenance alarms for action by these two groups as required.
At an early ISA-108 Committee meeting held in conjunction with ARC’s World Industry Forum in Orlando, Florida, earlier this year, the discussion focused on defining unambiguous terminology and models and developing use cases. The expectation is that this committee will not attempt to reinvent the wheel, but rather incorporate the best of related standards. For example, the committee is likely to incorporate Namur NE107 standard icons to identify different categories of device problems in terms of severity and root cause. End users should get involved and support this worthwhile effort.
For more information contact Paul Miller, ARC Advisory Group, +1 781 471 1126, pmiller@arcweb.com, www.arcweb.com
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