An Analytic Tool Box for Optimizing CBM Decisions
Andrew K.S. Jardine
Department of Mechanical & Industrial Engineering
University of Toronto
Engineers and asset managers associated with Operations and Maintenance (O&M) have to make many difficult decisions, and this presentation addresses one of them: the important maintenance tactic of condition-based maintenance (CBM). Historically, companies either waited until a piece of equipment failed before repairing or replacing it, or simply guessed at a good time to perform maintenance and hopefully avoid failure. With CBM, the guesswork is largely eliminated because equipment is closely monitored. Empirical proof of a change in condition now guides maintenance decisions. The problem now is an overabundance of information. With the fourth industrial revolution, Industry 4.0, data come from everywhere, and everything is linked to everything else. ISO 55001 stresses in Section 8.2.3: “The organization should have the capability to make evidence-based decisions on proposed changes and the ability to consider scenarios systematically across the entire organization.” This is all well and good, but if we want to make maintenance decisions based on evidence, which data are most relevant?
The focus of the presentation is evidence-based asset management (EBAM) and its application to CBM decisions. It illustrates the value of applying analytics to big data gathered by numerous condition monitoring technologies (oil sampling, vibration monitoring, pressure, temperature etc.) to ensure that an evidence-based decision is made and that the decision will, in fact, optimize the condition based maintenance decisions.
The methodology has been successfully applied in numerous sectors, such as the military, mining, transportation, pulp and paper, petrochemicals, food processing, and electricity generation.
Reliability in the 21st Century
William Q. Meeker
Department of Statistics
Center for Nondestructive Evaluation
Iowa State University
Ames, Iowa 50010
Reliability is an engineering discipline that relies heavily on the application of probability and statistics. Changes in sensor, communications, and storage technologies are changing the nature of reliability field data. An increasing number of modern systems are being outfitted with sensors that capture information about how and when and under what environmental and operating conditions individual systems are being used. In some cases, the physical/chemical state of critical system components can also be quantified and reported. For many systems such information is being downloaded continuously into data farms. In addition, improvements in computing capabilities and investment in developing physics-based models for failure provide another important dimension of reliability information. There are many potential applications for using such data to improve safety and reduce costs but existing statistical methods for reliability assessment and prediction are inadequate for the tasks. This talk reviews some particular applications where the modern field reliability data are used and explores some of the opportunities to use modern reliability data to provide stronger statistical/physical methods that can be used to operate and predict the performance of systems in the field. We also provide some examples of recent technical developments designed to be used in such applications and outline remaining challenges.
Key words: Condition-based maintenance, Dynamic covariates, Materials state awareness, Prognostics, Structural health monitoring