At ACHEMA 2015, industry associations ZVEI and NAMUR, in collaboration with ARC Advisory Group, organized a series of presentations and panel discussions, including one on information-driven asset performance in the O&M life cycle phase.
Information-Driven Asset Performance
By Valentijn de Leeuw Keywords
Information, Condition Monitoring, Predictive Analytics, Asset Lifecycle Information Management, ACHEMA, Mobility, Industrial Internet of Things (IIoT)
Overview
ACHEMA, held every three years in Germany, is the world's largest and most important event for the process industries. Topics covered include process engineering, laboratory technology, plant engineering and
con-struction, instrumentation, control, and automation, and I -- in recent years -- the cross-topical themes of biotechnology and environmental protection. In the hall dedicated to instrumentation, control, and automation solutions, the industry associations ZVEI and NAMUR, in collaboration with ARC Advisory Group, organized a series of presentations and panel discussions that were open to the general public. In this ARC Insight, we review the takeaways from the session on information-driven asset perfor-mance in the critical operate and maintain phase of the asset lifecycle. Asset Lifecycle Management’s Principles and Goals
Physical assets used in the process industries can range in scope and size from a single instrument or controller to a full plant or multi-plant complex. These assets represent a significant investment and it is generally accepted that their respective lifecycles must be managed to achieve optimal return. As discussed in detail in previous ARC reports, overall asset lifecycle man-agement (ALM) involves a set of interconnected, iterative processes: • In the design and build phase of a project, programs are managed for
• During the much longer operate and maintain (O&M) phase, the pro-cess focuses on asset performance management (APM).
• Asset and project portfolio management (APPM) aligns the overall portfolio of investments in assets with the company’s strategic objec-tives.
Model for ALM/ALIM Processes
Because of their interconnection, these processes require a continuous ex-change of information. This is achieved through asset lifecycle information management (ALIM). ALIM incorporates the collection, management and distribution of information on design and construction, operation and maintenance of the asset portfolio and makes the data available to PPM, APM, and APPM.
The overall purpose of these processes is to maximize the net total value of ownership over the asset's lifecycle. Yet the multi-parameter optimization for this process typically involves tradeoffs because of parameter interde-pendency. For example, if a plant is built faster and is in operation earlier, the project costs may be higher, but this is more than compensated by the additional value of products sold. And while maintenance costs can be
re-duced, this is also likely result in reduced productions and possibly shorten the lifetime of the asset. Organizations have opportunities to exploit this potential for global optimization. To reach the optimum, timely, in-context, high-quality information is required and must be transparent from the con-troller or sensor up to the highest level of optimization.
Asset Performance Management Issues and Solutions Experts participating in the discussions at ACHEMA were Peter Spelberg-Jahn, product manager for Siemens COMOS, Milo Scheeren, Chief Product Officer of BlueCielo ECM Solutions; Mike Brooks, CEO of Mtell; and Yosuke Ishii, Project Manager Mobile Solutions at Yokogawa.
ALIM Data Quality
Milo Scheeren of BlueCielo drew the audience's attention to the ALIM data quality risk. Among other issues, low data quality can result in mainte-nance inefficiencies, non-compliance with regulations, or create a safety
risk, as staff may make wrong decisions based on incomplete or incorrect data.
Full remediation can be expensive, as inspect-ing data for quality is tedious and costly. Scheeren recommends that focusing on the most important data might be more affordable and effective. He recommends that owner-operators structure data and documentation and compare these with reference data libraries from ISO standards. Dashboards representing data quality and the com-pleteness of documentation can help owner-operators assess urgencies and gaps and used as a basis to create a plan. He said that data quality man-agement can provide benefits in the range of 2 to 5 percent of the maintenance budget, in addition to longer term savings from operational improvements.
Peter Spelberg-Jahn, product manager for Siemens COMOS agreed with the importance of ALIM data quality. He noted that only important, often-used data needs to be structured. Rarely often-used, auxiliary data can remain unstructured. The challenge according to Spelberg-Jahn, is to know what is important and what isn't. Experience shows that this is highly dependent on the company and culture and cannot be derived in a straightforward
manner from equipment categories or other PLM criteria. This means own-er-operators need to asses which equipment or data have most impact on their performance based on their operating and maintenance strategies. A participant asked about the consequences of wrong data. Spelberg-Jahn explained that an object-oriented centralized data hub will show inconsist-encies and forces the user to decide which data is wrong and which is right. Peter indicated that good asset information management throughout the lifecycle can save up to 50 percent of engineering and maintenance time.
Predicting Asset Performance Degradation
Mike Brooks of Mtell explained how a new analytics solution can predict performance degradation of equipment weeks in advance using weak sig-nals that people would not have picked up on. In his experience, by the time problems are noticed, some damage has often already occurred. This compresses the time available to apply maintenance, leaving little option for scheduling both maintenance and related operational activities. The solution can also prescribe how to maintain equipment or adapt processes to avoid deterioration. He also reported cases in which users did not trust the first detection of an anomaly, ultimately resulting in equipment dam-age. Mr. Brooks used the term “process abuse” to indicate how the process can inadvertently be pushed for per-formance reasons without understanding that this can shorten the asset longevity.
Processes need to be changed to avoid recurring damage, he insisted. He reported a case in which the software helped reduce downtime from 72 to 94 percent. A transportation company applied the solution and gained $10 million in just three months. By extending the scope, the benefits amounted to $200 million in two years.
Evolving Automation Pyramid
Yosuke Ishii of Yokogawa explained how the automation pyramid is changing with new production methodologies such as modularization and
Internet of Things (IoT) applications. An increase in both high- and low-level data create opportunities for owner-operators.
Mr. Ishii mentioned that Yokogawa has identified nine use cases, most re-lated to mobile maintenance solutions. Cus-tomers have shown interest in combining augmented reality and mobile solutions to access equipment data, ERP data, and maintenance management information at the point where the equipment is located, thus helping to connect organizational sites. The company has implemented three cases to date, one that resulted in proven benefits of €20 to €50 thousand per annum.
Recommendations
As managing assets in industrial facilities becomes more complex, both from technical and organizational perspectives, owner-operators, engineer-ing procurement and construction company’s (EPCs), and other stakeholders should carefully design their collaborative processes. All par-ties need to define their responsibilipar-ties related to maintaining and transferring shared asset information and agree upon common quality standards. A strategy for the handover and exchange of information dur-ing the lifecycle must be defined usdur-ing the available ISO standards (15926, 18101, 14224).
Applying ISO 15926, 14224, and 18101 Standards for Asset Lifecycle Information Management
High-level processes must be complemented with modern applications, such as predictive and prescriptive analytics of both small (individual equipment-oriented) and big (equipment park-oriented) industrial data,
mobile applications, new integration schemes and technologies and be fueled using more abundant sensor information in close to real time. These measures should help optimize the total value of ownership or prof-itability for all stakeholders. They should be practical and effective and not be hindered by rigid principles or beliefs. For example Industrial IoT (IIoT) applications can complement existing automation networks and existing IT applications in plant or enterprise environments.
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