An ocean turbine extracts the kinetic energy from ocean currents to generate electricity. Machine Condition Monitoring (MCM) / Prognostic Health Monitoring (PHM) systems allow for self-checking and automated fault detection, and are integral in the construction of a highly reliable ocean turbine. This paper presents an onshore test platform for an ocean turbine as well as a case study showing how machine learning can be used to detect changes in the operational state of this plant based on its vibration signals.In the case study, seven widely used machine learners are trained on experimental data gathered from the test platform, a dynamometer, to detect changes in the machine's state. The classification models generated by these classifiers are being considered as possible components of the state detection module of an MCM/PHM system for ocean turbines, and would be used for fault prediction. Experimental results presented here show the effectiveness of decision tree and random forest learners on distinguishing between faulty and normal states based on vibration data preprocessed by a wavelet transform.
This paper considers the use of feature selection within the state detection module for an ocean turbine condition monitoring system. The goal is to reduce the quantity of data to be processed while maintaining or improving state detection capabilities. Five feature selection techniques (Chisquared, Information Gain, Signal-To-Noise, AUC and PRC) are evaluated based on their effects on four widely used machine learning algorithms, namely Naive Bayes, k-Nearest Neighbors, Decision Tree and Logistic Regression, when each machine learner is trained on the top n features selected by each feature selection technique. Six values of n (2, 4, 6, 8, 10 and 15) were considered. Features were extracted from the raw vibration signals using a Short Time Wavelet Transform with Baselining (STWTB) technique designed to allow for reliable state detection regardless of the turbine's operating conditions, which are often reflected within its vibration readings. The condition-independent features extracted by the STWTB are then fused to combine all the data observed by all sensor sources. Models were built on data gathered at one operating condition and tested against data from a different operating condition to simulate the problem of building models which work regardless of operating condition. Results show that kNearest Neighbors, Naive Bayes and Logistic Regression have improved classification performance when using less than 11% of the 78 available features, with Logistic Regression needing just 2 features selected by the Signal-To-Noise technique to generate a perfect classification model. The Decision Tree performed best without feature selection.
Class imbalance is prevalent in many real world datasets. It occurs when there are significantly fewer examples in one or more classes in a dataset compared to the number of instances in the remaining classes. When trained on highly imbalanced datasets, traditional machine learning techniques can often simply ignore the minority class(es) and label all instances as being of the majority class to maximize accuracy. This problem has been studied in many domains but there is little or no research related to the effect of class imbalance in fault data for condition monitoring of an ocean turbine. This study makes the first efforts in bridging that gap by providing insight into how class imbalance in vibration data can impact a learner's ability to reliably identify changes in the ocean turbine's operational state. To do so, we empirically evaluate the performances of three popular, but very different, machine learning algorithms when trained on four datasets with varying class distributions (one balanced and three imbalanced) to distinguish between a normal and an abnormal state. All data used in this study were collected from the testbed for an ocean turbine and were undersampled to simulate the different levels of imbalance. We find here, as in other domains, that the three learners seemed to suffer overall when trained on data with a highly skewed class distribution (with 0.1% examples in a faulty/abnormal state while the remaining 99.9% were captured in a normal operational state). It was noted, however, that the Logistic Regression and Decision Tree classifiers performed better when only 5% of the total number of examples were representative of an abnormal state (the remaining 95% therefore indicating normal operation) than they did when there was no imbalance present.
Data fusion is the process of combining data from multiple sources, allowing for a more complete and accurate assessment of a system or an environment than could have been otherwise provided by a single source. This paper considers and empirically evaluates a decision-level data fusion method for enabling reliable ocean turbine state detection based on data from multiple sensors. This method involves first generating a classification model from the data from individual sensor channels. For each new incoming instance, the probability that this new instance belongs to each of the possible system states is then computed individually based on observations made by each source. These probabilities are averaged and the system state with the highest probability is selected as the fused output. In a case study presented in this paper, six accelerometers mounted at different positions along a dynamometer test bed for an ocean turbine measure the vibration of various components while the machine is in operation. Each sensor gives unique information about the dynamometer thus ignoring data from one or more sources (or sensors) means possibly discarding useful information. We apply the decision-level fusion method to combine the decisions made from the individual channels and allow for more informed state detection which considers all sources together. Five popular machine learning algorithms are used to generate the classification models from the individual sensor data to see how the performances of each algorithm is affected by fusion and to determine whether this decision level fusion approach can lead to optimal behavior for an ocean turbine state detection module. All five learners are found to benefit from such an approach. Of all the learners, the k-Nearest Neighbors algorithm produces the best results after fusion.
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