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Complex devices are connected daily and eagerly generate vast streams ofmultidimensional state measurements. These devices often operate in distinctmodes based on external conditions (day/night, occupied/vacant, etc.), and toprevent complete or partial system outage, we would like to recognize as earlyas possible when these devices begin to operate outside the normal modes.Unfortunately, it is often impractical or impossible to predict failures usingrules or supervised machine learning, because failure modes are too complex,devices are too new to adequately characterize in a specific environment, orenvironmental change puts the device into an unpredictable condition. Wepropose an unsupervised anomaly detection method that creates a negative samplefrom the positive, observed sample, and trains a classifier to distinguishbetween positive and negative samples. Using the Contraction Principle, weexplain why such a classifier ought to establish suitable decision boundariesbetween normal and anomalous regions, and show how Integrated Gradients canattribute the anomaly to specific variables within the anomalous state vector.We have demonstrated that negative sampling with random forest or neuralnetwork classifiers yield significantly higher AUC scores than IsolationForest, One Class SVM, and Deep SVDD, against (a) a synthetic dataset withdimensionality ranging between 2 and 128, with 1, 2, and 3 modes, and with andwithout noise dimensions; (b) four standard benchmark datasets; and (c) amultidimensional, multimodal dataset from real climate control devices.Finally, we describe how negative sampling with neural network classifiers havebeen successfully deployed at large scale to predict failures in real time inover 15,000 climate-control and power meter devices in 145 Google officebuildings.

Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure