Reliability engineering methods are widely applied in design and manufacturing. The process of deploying this collection of tools appropriately is known as Design for Reliability (DFR). Some reliability engineering tools and methods have also been applied in the maintenance sector (i.e. equipment operators) but, in many cases, not as extensively. In this article, we will review the reliability methodologies that are applicable for asset performance management (APM) and propose a process for deploying the appropriate tools at the appropriate stages.
Reliability engineering is a discipline that combines practical experience, maintenance, safety, physics and engineering. Observational data is combined with experience to create models in order to understand the behavior of the equipment, optimize its performance and minimize the life cycle/operational costs. It is important to note that reliability engineering is not simply statistics and it is not always quantitative. Even though quantitative analysis plays a major role in the reliability discipline, many of the available tools and methods are also process-related. It is therefore useful to separate these methods and tools into quantitative and qualitative categories.
In the quantitative category, the typical tools are:
In the qualitative category, the typical tools are:
In this article, we will focus on some of the reliability engineering tools that are the most applicable in asset performance management. This will include a discussion of how and when each method should be deployed in order to maximize effectiveness.
Understanding when, how and where to use the wide variety of available reliability engineering tools will help to achieve the reliability mission of an organization. This is becoming more and more important with the increasing complexity of systems and sophistication of the methods available for determining their reliability. With increasing complexity in all aspects of asset performance management, it becomes a necessity to have a well-defined process for integrating reliability activities. Without such a process, trying to implement all of the different reliability activities involved in asset management can become a chaotic situation in which reliability tools may be deployed too late, randomly or not at all. This can result in the waste of time and resources as well as a situation in which the organization is constantly operating in a reactive mode.
Managers and engineers in the asset management discipline have come to this realization, and a push for a more structured process has been seen in recent years. The circumstances are very similar to what happened with the quality assurance discipline back in the 1980s, which spawned successful processes such as Six Sigma and Design for Six Sigma (DFSS). In more recent years, the same realization occurred in product development with the resulting Design for Reliability (DFR) process. It is therefore natural to look into these successful processes in order to create a process for asset performance management.
The process proposed in this article is based on the Design, Measure, Analyze, Improve and Control (DMAIC) methodology that is widely used in Six Sigma for projects aimed at improving an existing business process. It includes five phases:
To develop the new APM-focused process, we first determined the asset performance management activities within each of these phases. Then we identified the reliability methods and tools that pertain to each activity/phase.
The proposed process can be used as a guide to the sequence of deploying different reliability engineering tools in order to maximize their effectiveness and to ensure high reliability. The process can be adapted and customized based on the specific industry, corporate culture and existing processes. In addition, the sequence of the activities within the APM process will vary based on the nature of the asset and the amount of information available. It is important to note that even though this process is presented in a linear sequence, in reality some activities would be performed in parallel and/or in a loop based on the knowledge gained as a project moves forward. Figure 1 shows a diagram of the proposed process. Each phase in the process is briefly introduced in the following sections.