Markov chains are simple yet powerful mathematical structures to model temporally dependent processes. They generally assume stationary data, i.e., fixed transition probabilities between observations/states. However, live, real-world processes, like in the context of activity tracking, biological time series, or industrial monitoring, often switch behavior over time. Such behavior switches can be modeled as transitions between higher-level modes (e.g., running, walking, etc.). Yet all modes are usually not previously known, often exhibit vastly differing transition probabilities, and can switch unpredictably. Thus, to track behavior changes of live, real-world processes, this study proposes an online and efficient method to construct Evolving Markov chains (EMCs). EMCs adaptively track transition probabilities, automatically discover modes, and detect mode switches in an online manner. In contrast to previous work, EMCs are of arbitrary order, the proposed update scheme does not rely on tracking windows, only updates the relevant region of the probability tensor, and enjoys geometric convergence of the expected estimates. Our evaluation of synthetic data and real-world applications on human activity recognition, electric motor condition monitoring, and eye-state recognition from electroencephalography (EEG) measurements illustrates the versatility of the approach and points to the potential of EMCs to efficiently track, model, and understand live, real-world processes.

Machine learning (ML) is increasingly employed in real-world applications like medicine or economics, thus, potentially affecting large populations. However, ML models often do not perform homogeneously, leading to underperformance or, conversely, unusually high performance in certain subgroups (e.g., sex=female ∧ marital_status=married). Identifying such subgroups can support practical decisions on which subpopulation a model is safe to deploy or where more training data is required. However, an efficient and coherent framework for effective search is missing. Consequently, we introduce SubROC, an open-source, easy-to-use framework based on Exceptional Model Mining for reliably and efficiently finding strengths and weaknesses of classification models in the form of interpretable population subgroups. SubROC incorporates common evaluation measures (ROC and PR AUC), efficient search space pruning for fast exhaustive subgroup search, control for class imbalance, adjustment for redundant patterns, and significance testing. We illustrate the practical benefits of SubROC in case studies as well as in comparative analyses across multiple datasets.

End-of-line (EOL) tests are an important step to detect and respond to reliability issues that electric motors face. In addition to conventional signal processing methods to establish automated test systems, Artificial Intelligence (AI) and Machine Learning (ML) based methods in recent years, managed to become a major enabler for smart manufacturing thanks to advancements in hardware and software components. Inevitably, the importance of quality data made its way into considerations and requirements of automated fault detection and condition monitoring systems. In this regard, this study proposes an AI-based testing framework for electric motors. We provide information on the reasons of faults observed and a test procedure to detect them. We also give detailed specifications on hardware (sensors and data collection equipment), and provide a data architecture and analysis on properties of ML models that make sense to be used in such scenarios.

In general, dynamic systems are systems with time-dependent behavior. Dynamic systems are characterized by the non-stationary data sequences they emit. One particular way to model these non-stationary sequences is to consider them as a sequence of stationary segments, regimes, where each regime is separated by regime switching points from both the preceding and subsequent regimes. In system identification and monitoring applications, it is crucial to correctly and timely detect these regime switching points. One promising estimation method that may be used for detecting regime switching points is the stochastic learning based weak estimation (SLWE) method by Oommen and Rueda. We use SLWE for estimating First Order Markov (FOM) probabilities between symbols emitted by a system and for predicting regime switching points. A switching point is detected when the SLWE estimator unlearns, i.e., adapts estimates of FOM probabilities to new observations, such that the estimate re-converges to a new value that reflects, for the new regime, the FOM dependency of system output tokens. In experiments with a real Dataset for Human Activity Recognition, we see that our method has attractive efficiency (time and space) and similar accuracy compared with the state-of the-art. Experiments with synthetic data, where we controlled noise and Hamming distance between regimes, show promising accuracy for noise rates up to 25%, a rate at which accuracy of state-of-the-art methods deteriorates. Our method is flexible and can be configured to use not only FOM but also second-order and prior symbol probabilities, and combinations thereof.