Publications

Despite the increased sensor-based data collection in Industry 4.0, the practical use of this data is still in its infancy. In contrast, academic literature provides several approaches to detect machine failures but, in most cases, relies on simulations and vast amounts of training data. Since it is often not practical to collect such amounts of data in an industrial context, we propose an approach to detect the current production mode and machine degradation states on a comparably small data set. Our approach integrates domain knowledge about manufacturing systems into a highly generalizable end-to-end workflow ranging from raw data processing, phase segmentation, data resampling, and feature extraction to machine tool anomaly detection. The workflow applies unsupervised clustering techniques to identify the current production mode and supervised classification models for detecting the present degradation. A resampling strategy and classical machine learning models enable the workflow to handle small data sets and distinguish between normal and abnormal machine tool behavior. To the best of our knowledge, there exists no such end-to-end workflow in the literature that uses the entire machine signal as input to identify anomalies for individual tools. Our evaluation with data from a real multi-purpose machine shows that the proposed workflow detects anomalies with an average F1-score of almost 93%.

Predictive maintenance is an essential aspect of the concept of Industry 4.0. In contrast to previous maintenance strategies, which plan repairs based on periodic schedules or threshold values, predictive maintenance is normally based on estimating the time-to-failure of machines. Thus, predictive maintenance enables a more efficient and effective maintenance approach. Although much research has already been done on time-to-failure prediction, most existing works provide only specialized approaches for specific machines. In most cases, these are either rotary machines (i.e., bearings) or lithium-ion batteries. To bridge the gap to a more general time-to-failure prediction, we propose a generic end-to-end predictive maintenance methodology for the time-to-failure prediction of industrial machines. Our methodology exhibits a number of novel aspects including a universally applicable method for feature extraction based on different types of sensor data, well-known feature transformation and selection techniques, adjustable target class assignment based on fault records with three different labeling strategies, and the training of multiple state-of-the-art machine learning classification models including hyperparameter optimization. We evaluated our time-to-failure prediction methodology in a real-world case study consisting of monitoring data gathered over several years from a large industrial press. The results demonstrated the effectiveness of the proposed methodology for six different time-to-failure pre-diction windows, as well as for the downscaled binary prediction of impending failures. In this case study, the multi-class feed-forward neural network model achieved the overall best results.

In many areas of decision making, forecasting is an essential pillar. Consequently, there are many different forecasting methods. According to the "No-Free-Lunch Theorem", there is no single forecasting method that performs best for all time series. In other words, each method has its advantages and disadvantages depending on the specific use case. Therefore, the choice of the forecasting method remains a mandatory expert task. However, expert knowledge cannot be fully automated. To establish a level playing field for evaluating the performance of time series forecasting methods in a broad setting, we propose Libra, a forecasting benchmark that automatically evaluates and ranks forecasting methods based on their performance in a diverse set of evaluation scenarios. The benchmark comprises four different use cases, each covering 100 heterogeneous time series taken from different domains. The data set was assembled from publicly available time series and was designed to exhibit much higher diversity than existing forecasting competitions. Based on this benchmark, we perform a comprehensive evaluation to compare different existing time series forecasting methods.

Due to the increasing size of today's data centers as well as the expectation of 24/7 availability, the complexity in the administration of hardware continuously icreases. Techniques as the Self-Monitoring, Analysis, and Reporting Technology (S.M.A.R.T.) support the monitoring of the hardware. However, those techniques often lack algorithms for intelligent data analytics. Especially, the integration of machine learning to identify potential failures in advance seems to be promising to reduce administration overhead. In this work, we present three machine learning approaches to (i) identify imminent failures, (ii) predict time windows for failures, as well as (iii) predict the exact time-to-failure. In a case study with real data from 369 hard disks, we achieve an F1-score of up to 98.0% and 97.6% for predicting potential failures with two or multiple time windows, respectively, and a hit rate of 84.9% (with a mean absolute error of 4.5 hours) for predicting the time-to-failure.

Due to the fast-paced and changing demands of their users, computing systems require autonomic resource management. To enable proactive and accurate decision-making for changes causing a particular overhead, reliable forecasts are needed. In fact, choosing the best performing forecasting method for a given time series scenario is a crucial task. Taking the "No-Free-Lunch Theorem" into account, there exists no forecasting method that performs best on all types of time series. To this end, we propose an automated approach that (i) extracts characteristics from a given time series, (ii) selects the best-suited machine learning method based on recommendation, and finally, (iii) performs the forecast. Our approach offers the benefit of not relying on a single method with its possibly inaccurate forecasts. In an extensive evaluation, our approach achieves the best forecasting accuracy.

Forecasting is an important part of the decision-making process and used in many fields like business, economics, finance, science, and engineering. According to the No-Free-Lunch-Theorem from 1997, there is no general forecasting method, that performs best for all time series. Instead, expert knowledge is needed to decide which forecasting method to choose for a specific time series with its own characteristics. Since a trial and error approach is very inefficient and expert knowledge is useful but a time-consuming task that cannot be fully automated, we present a new hybrid multi-step-ahead forecasting approach based on time series decomposition. Initial evaluations show that this hybrid approach improves the forecast accuracy compared to six existing forecasting methods while maintaining a short runtime.

Resource demands are crucial parameters for modeling and predicting the performance of software systems. Direct measurement of these resource demands is usually infeasible due to instrumentation overheads causing measurement interferences and perturbation in production environments. Thus, a number of statistical estimation approaches (e.g., based on optimization, regression or Kalman filters) have been proposed in the literature. Most of these approaches are parameterized. These parameters influence the estimation quality and the required computation time. Existing work uses historical data as training sets to optimize those parameters and to minimize the estimation error of those approaches. However, if the data traces are fundamentally different, the optimal parameter settings are different as well. In this paper, we propose to use automated clustering in order to group training sets into groups of similar optimization behavior. This way, optimization can be specifically tailored to certain groups of traces in a self-aware manner. During run-time, every trace is first sorted into a cluster, where the respective cluster-wide parameter optimum can be applied. A preliminary case study shows that clustering can provide promising improvements.