Log-based predictive maintenance

Sipoš, Ruben; Fradkin, Dmitriy; Moerchen, Fabian; Wang, Zhuang

doi:10.1145/2623330.2623340

Cited by 158 publications

(84 citation statements)

References 21 publications

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“…The ability to classify objects of interest from a training set, whether those objects are terrorists (1), machines that need maintenance (2), or emails containing a malicious link (3), represents the greatest success in the field. Typically, no single machine learning algorithm does everything well.…”

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confidence: 99%

Predicting human behavior: The next frontiers

Subrahmanian

Kumar

2017

Science

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Machine learning has provided researchers with new tools for understanding human behavior. In this article, we briefly describe some successes in predicting behaviors and describe the challenges over the next few years.A dvances in machine learning are revolutionizing how we understand offline and online human behavior. The ability to classify objects of interest from a training set, whether those objects are terrorists (1), machines that need maintenance (2), or emails containing a malicious link (3), represents the greatest success in the field. Typically, no single machine learning algorithm does everything well. Although accuracy is crucial, the acceptable accuracy varies with the problem being studied, and accuracy is not enough. All too often, researchers explain why their predictions are right but say nothing about why their predictions might be wrong. Knowing both enables decision-makers to make better decisions. Especially in high-risk situations, predictions must have accompanying explanations that provide deeper understanding of the situation being studied. A predictive model must also provide one or more prescriptions for potential future actions that enable decision-makers to make better decisions. Today's machine learning methods do not necessarily satisfy these three criteria. What constitutes an ideal predictive algorithm depends on the application. Oftentimes, stakeholders (e.g., social media platforms and search engines) will use varying definitions of accuracy that meet their particular needs. Moreover, domain experts may use extensive knowledge of the domain to suggest relevant independent variables to be included in a data set. Often, they will explain predictions using both the technical accuracy measures generated by a predictive model and stories from their discipline that are more understandable to their audiences. All of this suggests that in real-world systems, computer scientists need to team with stakeholders to generate high-impact results.In our opinion, the next generation of predictive models must deal with four major challenges.First, the maxim that more data lead to better predictive models is not always true, because noise in the data can overwhelm predictive models. The ability to deal with noisy, incomplete, and inconsistent data will be at the heart of nextgeneration predictive models. For instance, when identifying "bots" on Twitter (4) that are seeking to sway opinion to be positive about a political candidate, we needed to ignore the huge numbers of bots that were seeking to achieve other ends-such as spreading spam or seeking to influence opinions about other topics or to deceive users into clicking on links that generate revenue for the person who included that link in their tweet. Moreover, data about many Twitter handles are limited and, in some cases, intentionally misleading. Bot developers go to considerable effort to ensure that their bots elude detection.A second challenge is that of rare-event prediction. For instance, companies monitoring their internal networ...

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confidence: 99%

Predicting human behavior: The next frontiers

Subrahmanian

Kumar

2017

Science

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“…A similar claim that the scheduled maintenance does not always help avoid the failures and degradation in performance is also presented in [22], in relation to the hardware systems. The work is concerned with log-based predictive analysis in order to monitor the conditions of the operating equipments and provide timely maintenance.…”

Section: Aging and Degradation Analysismentioning

confidence: 63%

Efficient Long-Term Degradation Profiling in Time Series for Complex Physical Systems

Ulanova

Yan

Chen

et al. 2015

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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The long term operation of physical systems inevitably leads to their wearing out, and may cause degradations in performance or the unexpected failure of the entire system. To reduce the possibility of such unanticipated failures, the system must be monitored for tell-tale symptoms of degradation that are suggestive of imminent failure. In this work, we introduce a novel time series analysis technique that allows the decomposition of the time series into trend and fluctuation components, providing the monitoring software with actionable information about the changes of the system's behavior over time. We analyze the underlying problem and formulate it to a Quadratic Programming (QP) problem that can be solved with existing QP-solvers. However, when the profiling resolution is high, as generally required by real-world applications, such a decomposition becomes intractable to general QP-solvers. To speed up the problem solving, we further transform the problem and present a novel QP formulation, Non-negative QP, for the problem and demonstrate a tractable solution that bypasses the use of slow general QP-solvers. We demonstrate our ideas on both synthetic and real datasets, showing that our method allows us to accurately extract the degradation phenomenon of time series. We further demonstrate the generality of our ideas by applying them beyond classic machine prognostics to problems in identifying the influence of news events on currency exchange rates and stock prices. We fully implement our profiling system and deploy it into several physical systems, such as chemical plants and nuclear power plants, and it greatly helps detect the degradation phenomenon, and diagnose the corresponding components.

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“…In a recent work, Sipos et al (2014) use equipment logs from medical scanners to predict failures using multi-instance learning (Dietterich et al 1997). In multi-instance learning the learner receives a set of bags which are labelled positive or negative.…”

Section: Supervised Methodsmentioning

confidence: 99%

Sequential anomalies: a study in the Railway Industry

2016

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Concerned with predicting equipment failures, predictive maintenance has a high impact both at a technical and at a financial level. Most modern equipments have logging systems that allow us to collect a diversity of data regarding their operation and health. Using data mining models for anomaly and novelty detection enables us to explore those datasets, building predictive systems that can detect and issue an alert when a failure starts evolving, avoiding the unknown development up to breakdown. In the present case, we use a failure detection system to predict train door breakdowns before they happen using data from their logging system. We use sensor data from pneumatic valves that control the open and close cycles of a door. Still, the failure of a cycle does not necessarily indicates a breakdown. A cycle might fail due to user interaction. The goal of this study is to detect structural failures in the automatic train door system, not when there is a cycle failure, but when there are sequences of cycle failures. We study three methods for such structural failure detection: outlier detection, anomaly detection and novelty detection, using different windowing strategies. We propose a two-stage approach, where the output of a point-anomaly algorithm is post-processed by a low-pass filter to obtain a subsequence-anomaly detection. The main result of the two-level architecture is a strong impact in the false alarm rate.

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Log-based predictive maintenance

Cited by 158 publications

References 21 publications

Predicting human behavior: The next frontiers

Predicting human behavior: The next frontiers

Efficient Long-Term Degradation Profiling in Time Series for Complex Physical Systems

Sequential anomalies: a study in the Railway Industry

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