System overview

Figure 1.1outlines a simplified version of the system considered in this thesis.

The simple form corresponds to the way new examples would face the system, but omits all the information feedback on model sizes, classification thresholds etc. In general this thesis deals with the three last boxes, as the choice of sensors and signal conditioning was done by project partners.

8 Condition Monitoring and Management from Acoustic Emissions

To prepare for the following chapters, a quick introduction to the 5 boxes in Figure 1.1follows.

Sensors, chapter 2

The sensors acquire ultrasonic stress waves on the outside of the cylinder liner/cover. These stress waves are generated by micro cracking in the material. The frequency range is 100 kHz - 1.25 MHz

Signal conditioning,chapter 2

The bandwidth of the signals are reduced to 10 kHz by root mean squaring.

The new signal is not the actual waveform but the energy envelope signal.

Preprocessing, chapter 2and 3

The signals are transformed from time to crank angle domain. The timing of engine related events can be aligned.

Modeling, chapter 4

A model is trained on preprocessed normal condition examples. The nor-mal and faulty examples separates in a one-dimensional feature when the model is applied to examples. E.g., the model knows the normal pat-tern and as the faulty patpat-tern gradually emerges, the deviation measure gradually increases.

Detection,chapter 5

The property of the model output should be that the values for normal and faulty examples should be separable. The simplest classifier, which is the one considered here, the detecting the fault is a matter of detecting that the one dimensional measure has crossed the threshold.

The more detailed Figure 1.2also outline the information flow in the opposite direction. Examples processed by some parameters are propagated through the whole system, and the best set of parameters are selected and sent back to the respective processing blocks (the red arrows). In that figure only two parameters are considered, the size of the models and the classification threshold. The same underlying approach extends to selecting model families, feature extraction methods and even sensors types where decisions based on the whole application are sent back to the blocks where it belong.

It should also be noted that some methods can be used in different ways in different blocks, for instance canPCA be applied as a feature extraction prior to other models that use the reduced feature subspace as their input. OrPCA can be used as it is mainly used here as a generative model that describe the observed data.

1.3 This thesis 9

Figure 1.1: The simplified information flow gives a general system overview. Sensors are attached on the monitored specimen, the signals obtained are conditioned before preprocessing, and finally detection based on the output of the modeling. This is only the general structure, as some methods like the component analysis methods can do both preprocessing and modeling at the same time.

Figure 1.2: A slightly more complicated information flow that show how some parame-ters are obtained using independent training sets. The three first blocks ofFigure 1.1 have been omitted for simplicity. The two example pools contain normal and faulty examples. Two parameters, the size of the model and a classification threshold, in the models are optimized by measuring the performance. The model is only trained on the normal condition. Only the last row, the performance evaluators know the true labels of the examples, the rest of the model only see the observed signals. When it comes to applying the optimized model to new examples we are back to the simpler straightforward system inFigure 1.1.

10 Condition Monitoring and Management from Acoustic Emissions

Chapter 2

Acquisition and pre-processing

This chapter describe the data acquisition setup, and the acoustic emission (AE) signals that are acquired with the system from the engine. Further I outline the preprocessing that makes theAEsignals computationally usable as input signals to the condition monitoring system.

2.1 Experimental data

The experimental data used for illustrative purposes here is a destructive test due to MAN B&W Diesel A/S. The data set consist of two three load condi-tions, 25%, 50% and 75%. The fault condition is induced by obstructing the application of lube oil inside the monitored cylinder while the engine was at 25% load. The other cylinders got lube oil during the whole the experiment, and since the cylinders are connected through the bottom oil “bathtub” some oil was sucked up in each cycle with the fresh air.

It was quickly discovered that the operational condition changes was problem-atic. Initially only the period referred to as Experiment 1 in Figure 2.1 was considered, since only one change occurs in this period - the shutdown of the lube oil system. The period labeled Experiment 2 refers to an additional faulty

12 Acquisition and pre-processing

Figure 2.1: Time line of main experiment conducted at MAN B&W Diesel

condition occurs while the lube oil system was turned off. This unintended fault occurred in the attached water brake, which controls the loading of the engine.

Luckily, it was temporary during an otherwise stable condition and it revealed interesting properties on model and sensor selection.