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We use unsupervised machine learning approaches in a completely data-driven binarization routine for vibration sensors with minimal lag. Gyroscopic vibration sensors are inherently noisy as they report analog signals that must be translated into digital values, in our case whether a pump is running (“on”) or not (“off”). When analyzing data from different pumps, each of which has their own baseline vibration values and magnitude of vibration, manual annotation is not feasible. We have tested multiple unsupervised methods including k-means clustering, Gaussian naïve Bayes, and ensemble learning to correctly binarize the analog signals. Comparisons are made on the basis of “blips” or times where the algorithm predicted an incorrect state for a short period of time before returning to the current state. This provides an objective metric with which to evaluate an algorithm’s success at binarizing the signal. We present results from an experimental design to probe the efficacy of different learning methods across data collected from ten vibration sensors deployed on pumps in a water treatment plant. We use initial k-means clustering for many algorithms to get an initial guess of the on or off state of the pump. From there we use a variety of smoothing, Gaussian naïve Bayes classifiers, and ensemble learning to get a final classification of pump activity. We apply these methods to data collected from ten sensors deployed on distinct pumps.
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