Condition monitoring is a well-known task to human beings - we scan the en-vironment for changes continuously, even while performing other tasks. We are alerted by unexpected sounds, movements, and even patterns in our environ-ment. We compare what we observe to the knowledge of how it appeared 2 minutes, hours, or days ago. Abrupt changes such as unexpected sounds from your child, car, bicycle, and CD player alerts you. Traditionally specialized helpers such as dogs barking at approaching people, canaries in coalmines, con-cealed wires connected to bells have been used. With gradual changes, the best helper is sometimes just a pair of fresh eyes/ears that are not been accustomed to the slow drift. In the mechanical world the condition monitoring task have been performed by the skilled people that operate the machinery on a regular basis. The engine operator will gradually learn how the engine sounds in differ-ent operational settings. In addition, we are pursuing this capability with the signal processing and learning framework.
1.1.1 Maintenance strategies
A main theme of the Condition Monitoring and Diagnostic Engineering Ma-nagement (COMADEM) conferences that I attended in 2003 and 2004, was profitability of condition based monitoring. Not all parts and faults are worthy of a condition monitoring system; the simplest example is that you don’t need a red light to tell you that a light bulb will break in 5 minutes - at least not at home. However when running an airport, you need a system that monitors the percentage and spatial distribution of broken bulbs in the runway system due to requirements given by the International Air Transport Association. Essentially the necessary monitoring level does not depend on the type of the part, but on the impact of its failure.
Although the management strategies are not a part of this thesis, I will outline my understanding of four such strategies. I will differ between failure based maintenance, scheduled based maintenance, condition based maintenance and Prescription based Health Management:
- No maintenance at all. Theuse until destroyed strategy is not that relevant with large diesel engines.
a With a failure based maintenance strategy the machinery is operating until the fault occurs. Then the fault is fixed and the machinery goes into operation again, e.g., change the bulb and turn the light on again.
1.1 Setting the stage 3
b With a schedule based maintenance strategy the different parts are inspected or even replaced after a specific number running hours, e.g., a bearing is replaced after 3000 running hours even if it looks normal. The replacement times differs from part to part, and is generally set very conservative such that the number of components that do fail within the specified time interval is very small. This strategy is for instance used within aviation industry, ship propulsion, as well as cars: as the oil should be changed every 15.000 km or year whatever comes first.
c With a condition based maintenance strategy, parts are replaced and repaired when a fault is expected to happen within a near future based on the health of the machine.
d With a prescription based health management system, the usage pattern and expected usage pattern is taken into consideration when scheduling mainte-nance. It is as computer war-games, e.g., each unit has an associated health bar, and the commanders (you + computer AI) are considering: Can this mission be fulfilled with that vehicle? Moreover, is it still usable afterwards?
The optimal strategy depends on a wide range of diverse and coupled parame-ters, where I guess economic and safety issues has the greatest impact when dealing with ship propulsion condition monitoring (CM). The common belief is that increasing the level of CMconstitutes an economic improvement, the first by repairing, later preventing many failures with the scheduled replacement of selected parts, that furthermore prevent the additional and often more severe faults caused by the original fault. The third improvement is achieved by im-proving the availability of the machinery, since maintenance is only scheduled when actually required. However these improvements have an associated cost:
Strategybrequire that the machine is taken out of service on a regular scale, and before that the proper replacement intervals should be determined. Strategy c require that the condition can be monitored in a reliable way, which most likely requires sensors, acquisition boards and specialized signal processing as well as knowledge on how the condition evolves. With strategydthe current condition and the expected usage are combined to give allow for trade off between risk of failure, success without recovery etc. Compared to the other strategies this also require analysis of wear as function of usage.
1.1.2 Monitoring strategies
The core of this thesis is “How to turn the observed acoustic emission energy (AEE) time series into a condition monitoring output”. Among the others we need to select the type of output, again they are order by increased “complexity”
4 Condition Monitoring and Management from Acoustic Emissions
1. Fault alarm, after the fault occurred 2. Late warning, just before it faults
3. Early warning, possibly with some failure time horizon
4. Wear level continuously monitored, more accurate failure time horizon
As with the maintenance strategies, systems that are more sophisticated are required to reach a higher performance level. Obviously, the longer time before the breakdown, the smaller are the deviations we are looking for and thus the harder we must work with features and algorithms find them.
Another issue on whether the monitoring system should model the normal con-dition and/or specific faults. In this thesis, only the normal concon-dition is modeled and deviations from the normal model are therefore just faults. In a previous EC project Fog used neural networks trained on some specific faults to classify other instances of those specific faults [Fog,1998]. This relates to the difference between unsupervised and supervised learning. On whether the model is given examples with or without associated labels during training. The labels tell the system that this is how the normal ones look like; and this is how the faulty ones looks like - and then we ask what this is? Without the labels the system is not told, but still expected to be discriminate between classes. It is like giving apples and oranges to a child. If you first tell this is an apple, this is an orange the child should get the idea. If you do not tell, the child might just say: fruit!
This thesis deals with giving the child apples with no labels, and then expect it to say “not apple” when given an orange. But in this case we actually we don’t know if the child is given both green and red apples to begin with. . .
As experiments conducted within the AEWATT project, have not been repro-duced and scuffing has not been encountered, it is unlikely that an accurate failure time horizon statistics can be achieved with those available data sets.
Simply our knowledge on how the fault emerges in the engine is not good enough.
Further, we cannot be sure if the way the fault is induced is also the way the fault appears outside the laboratory.
In this thesis only the normal condition is considered for modeling, thus faults will just be labeled as faults. It is possible to add new models and slowly build a supervised system. This requires that the relevant data is saved when a fault occur, and that this package forwarded to experts for diagnosing that could lead to new models that diagnose this fault. If storage permits, also scheduled acquisitions could be considered.