Interview with Andrew Jardine
Q: Professor Jardine, why did you develop EXAKT?
A: Condition based maintenance is the most desirable form of maintenance, yet, former students, now maintenance professionals, told me that they find often that their current CBM programs, such as oil analysis, don’t deliver the intended results. I asked how “exactly” their staff interpreted condition monitoring data. In other words, how did they decide whether or not to remove an item for repair? Their answers led me to investigate whether a more rigorous decision methodology might improve the payback on the rather large investment they were making in condition based maintenance.
Q: How was condition monitoring data being interpreted?
A: I found that two approaches were being used. One method arrived at decisions by recalling solid experience and engineering knowledge that, for example, a known level of a monitored variable indicates the initiation of a particular failure mode. The second, relied on “trend analysis” as the basis for making the “maintain-now-or-continue-operating” decision. Looking closely at the data from both cases, I found that, while the former achieved, generally, the expected benefits, the latter failed to provide measurable return on the investment and running cost of the CBM program.
Q: Why was that?
A: In the first case, CBM detection of, for example, diesel fuel in lubricating oil, reflects the “ground truth” of a failed condition – that is, a leaking of fuel past the sealing surfaces of some interface, perhaps the piston, ring, and cylinder wall. Similarly, coolant in the lube oil, reflects the breakdown of some interface, possibly a gasket, separating the cooling and lubricating fluids. However, where “data trending” is the principal method for decisions, the relationship between monitored data and the failure mechanism is often vague. We rely on a palpable deviation from some “normal” trend to alert us to a problem.
Q: That sounds reasonable. Doesn’t it work?
A: It could, if the data were clearly reflective of a developing failure.
Q: Isn’t it?
A: No, not clearly so. Usually, several separate or inter-related phenomena affect the monitored data. Although common sense would have us believe that monitored signals from the machine must contain its health information, we know little about the nature of that relationship. For example, if the operator of a thermal nuclear reactor alters the temperature of the sealing fluid in the reactor pump, then the leak rate, normally used to monitor seal health, would tend to decrease, even if the seal were, indeed, beginning to fail. The interpretation of trends, thus, becomes complicated. Add to this, random noise, the effects of fluctuating load cycles, and several failure modes, and you can imagine that attempting trend analysis of multiple data streams, emanating from complex systems, might frustrate the well-intentioned maintenance planner or engineer.
Q: I see. How did you go about resolving this problem?
A: The condition monitoring phrase “Equipment Health” brings to mind the idea of human health. I looked at the medical field where the problem of symptoms based prognostics is well known. The concept of “risk factors” that associate medical test results with specific illnesses seemed perfectly analogous to the problem of risk based decisions in maintenance. Cox’s proportional hazard model in the 1970’s had proved useful in the detection of illnesses and in the prediction of human survival. I applied these ideas, first, to jet propulsion engines, and discovered that we could model the risk of engine failure in terms of the oil analysis results of iron and chromium, and the engine’s accumulated flight hours since overhaul. That work proved very encouraging. So much so, that we set out to develop a general purpose software platform for PHM (proportional hazard modeling) prediction. Over the past decade, at the CBM laboratory of the University of Toronto, we gradually improved the program by applying it to many industrial CBM situations. It has reached the stage now, where it should be made commercially available to the mainstream of the physical asset management community. That is the reason OMDEC was spun off from the CBM lab.
Q: Why did you call the program “EXAKT”? Are you implying that CBM is an “exact” science? Isn’t EXAKT based on probabilities and statistics?
A: I can see why you think that the name “EXAKT” and the probabilistic nature of failure are incongruous. Most managers intuitively understand risk. They instinctively weigh probabilities when making decisions in the normal course of their activities. If they were told “exactly” the risk levels associated with alternative decisions, they would find such information helpful indeed. Otherwise stated, if they knew “exactly” with what level of confidence they may accept a residual life estimate for some operating physical asset, they could adjust their operational and maintenance plans accordingly.
Q: It is generally thought that EXAKT is a difficult program to use.
A: Self doable, tutorial exercises are a good way to provide a comfort factor to potential users. EXAKT, is actually a usable tool. But, because EXAKT evolved as a research platform, some people have formed the impression that it is too difficult for them. OMDEC has set out to dissolve that impresssion. They have published a series of step-by-step tutorials that convey many common data problem solving techniques.
Q: Can anyone procure a demo copy of the software and the tutorials you speak of?
A: Yes, on the OMDEC website, www.omdec.com. OMDEC also provides free limited technical support for potential implementors.
Thank you, Professor Jardine