Acoustic-based fault detection has been one of the key instruments to monitor the health condition of mechanical parts. However, the background noise of an industrial environment may negatively influence the performance of fault detection. Limited attention has been paid to improving the robustness of fault detection against industrial environmental noise. Therefore, we present the Lenze production background-noise (LPBN) real-world dataset and an automated and noise-robust auditory inspection (ARAI) system for the end-of-line inspection of geared motors. An acoustic array is used to acquire data from motors with a minor fault, major fault, or which are healthy. A benchmark is provided to compare the psychoacoustic features with different types of envelope features based on expert knowledge of the gearbox. To the best of our knowledge, we are the first to apply time-varying psychoacoustic features for fault detection. We train a state-of-the-art one-class-classifier, on samples from healthy motors and separate the faulty ones for fault detection using a threshold. The best-performing approaches achieve an area under curve of 0.87 (logarithm envelope), 0.86 (time-varying psychoacoustics), and 0.91 (combination of both).

Configurable software systems have become increasingly popular as they enable customized software variants. The main challenge in dealing with configuration problems is that the number of possible configurations grows exponentially as the number of features increases. Therefore, algorithms for testing customized software have to deal with the challenge of tractably finding potentially faulty configurations given exponentially large configurations. To overcome this problem, prior works focused on sampling strategies to significantly reduce the number of generated configurations, guaranteeing a high t-wise coverage. In this work, we address the configuration sampling problem by proposing a deep reinforcement learning (DRL) based sampler that efficiently finds the trade-off between exploration and exploitation, allowing for the efficient identification of a minimal subset of configurations that covers all t-wise feature interactions while minimizing redundancy. We also present the CS-Gym, an environment for the configuration sampling. We benchmark our results against heuristic-based sampling methods on eight different feature models of software product lines and show that our method outperforms all sampling methods in terms of sample size. Our findings indicate that the achieved improvement has major implications for cost reduction, as the reduction in sample size results in fewer configurations that need to be tested.

In the pharmaceutical industry the screening of opaque vaccines containing suspensions is currently a manual task carried out by trained human visual inspectors. We show that deep learning can be used to effectively automate this process. A moving contrast is required to distinguish anomalies from other particles, reflections and dust resting on a vial's surface. We train 3D-ConvNets to predict the likelihood of 20-frame video samples containing anomalies. Our unaugmented dataset consists of hand-labelled samples, recorded using vials provided by the HAL Allergy Group, a pharmaceutical company. We trained ten randomly initialized 3D-ConvNets to provide a benchmark, observing mean AUROC scores of 0.94 and 0.93 for positive samples (containing anomalies) and negative (anomaly-free) samples, respectively. Using Frame-Completion Generative Adversarial Networks we: (i) introduce an algorithm for computing saliency maps, which we use to verify that the 3D-ConvNets are indeed identifying anomalies; (ii) propose a novel self-training approach using the saliency maps to determine if multiple networks agree on the location of anomalies. Our self-training approach allows us to augment our data set by labelling 217,888 additional samples. 3D-ConvNets trained with our augmented dataset improve on the results we get when we train only on the unaugmented dataset.

Article Free Access Share on Instruction level profiling and evaluation of the IBM/6000 Authors: Chriss Stephens Center for Dependable Systems, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA Center for Dependable Systems, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PAView Profile , Bryce Cogswell Center for Dependable Systems, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA Center for Dependable Systems, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PAView Profile , John Heinlein Center for Dependable Systems, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA Center for Dependable Systems, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PAView Profile , Gregory Palmer Center for Dependable Systems, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA Center for Dependable Systems, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PAView Profile , John P. Shen Center for Dependable Systems, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA Center for Dependable Systems, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PAView Profile Authors Info & Claims ISCA '91: Proceedings of the 18th annual international symposium on Computer architectureApril 1991Pages 180–189https://doi.org/10.1145/115952.115971Published:01 April 1991Publication History 30citation385DownloadsMetricsTotal Citations30Total Downloads385Last 12 Months38Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF