Exploratory Study of Machine Learning Techniques for Supporting Failure Prediction

Campos, Joao R.; Vieira, Marco; Costa, Ernesto

doi:10.1109/edcc.2018.00014

Cited by 15 publications

(12 citation statements)

References 20 publications

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“…There are many solutions to this problem. Among others, undersampling can be used as authors in [3]. Alternatively, class weighting can be applied to increase or decrease algorithm's sensitivity towards specific classes.…”

Section: Prediction Models and Validationmentioning

confidence: 99%

Performance Comparison of Machine Learning Algorithms for Predictive Maintenance

Gęca¹

2020

IAPGOS

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The consequences of failures and unscheduled maintenance are the reasons why engineers have been trying to increase the reliability of industrial equipment for years. In modern solutions, predictive maintenance is a frequently used method. It allows to forecast failures and alert about their possibility. This paper presents a summary of the machine learning algorithms that can be used in predictive maintenance and comparison of their performance. The analysis was made on the basis of data set from Microsoft Azure AI Gallery. The paper presents a comprehensive approach to the issue including feature engineering, preprocessing, dimensionality reduction techniques, as well as tuning of model parameters in order to obtain the highest possible performance. The conducted research allowed to conclude that in the analysed case , the best algorithm achieved 99.92% accuracy out of over 122 thousand test data records. In conclusion, predictive maintenance based on machine learning represents the future of machine reliability in industry.

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Section: Prediction Models and Validationmentioning

confidence: 99%

Performance Comparison of Machine Learning Algorithms for Predictive Maintenance

Gęca¹

2020

IAPGOS

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“…Machine learning has been extensively used for failure detection [8], [28], [30], [32], attack prediction [1], [3], [4], [19], [20], [48], and face recognition [35], [37], [42]. Considering noisy labels in classification algorithms is also a problem that has been explored in the machine learning community as discussed in [5], [12], [24].…”

Section: Related Workmentioning

confidence: 99%

Robust Anomaly Detection on Unreliable Data

Zhao

Cerf

Birke

et al. 2019

2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

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Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the wild can be unreliable due to careless annotations or malicious data transformation for incorrect anomaly detection. In this paper, we present a two-layer on-line learning framework for robust anomaly detection (RAD) in the presence of unreliable anomaly labels, where the first layer is to filter out the suspicious data, and the second layer detects the anomaly patterns from the remaining data. To adapt to the on-line nature of anomaly detection, we extend RAD with additional features of repetitively cleaning, conflicting opinions of classifiers, and oracle knowledge. We on-line learn from the incoming data streams and continuously cleanse the data, so as to adapt to the increasing learning capacity from the larger accumulated data set. Moreover, we explore the concept of oracle learning that provides additional information of true labels for difficult data points. We specifically focus on three use cases, (i) detecting 10 classes of IoT attacks, (ii) predicting 4 classes of task failures of big data jobs, (iii) recognising 20 celebrities faces. Our evaluation results show that RAD can robustly improve the accuracy of anomaly detection, to reach up to 98% for IoT device attacks (i.e., +11%), up to 84% for cloud task failures (i.e., +20%) under 40% noise, and up to 74% for face recognition (i.e., +28%) under 30% noisy labels. The proposed RAD is general and can be applied to different anomaly detection algorithms.

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“…Among them, a method based on clustering the system state and the use of Hidden Semi-Markov Models to predict failure-prone system states was proposed [12], and SVMs were used to predict failures of hard drives [13]. More recently, we conducted an exploratory study on using different ML algorithms and techniques for OFP [9]. However, while relevant, this work focused on studying the performance of various ML algorithms and techniques, without configuring and tuning the models thoroughly.…”

Section: A the Failure Prediction Conceptmentioning

confidence: 99%

“…Concerning the pair t l , t p this work focuses only on the configuration [40,20], as it was the one that systematically achieved the best results in our previous work (among those studied, [20,20], [40,20], and [60,20], for a ''short'', ''medium'', and ''long'' term prediction) [9]. This configuration was used to label the data according to the approach proposed in [5].…”

Section: B Datasetsmentioning

confidence: 99%

“…Although some of the most well-known ensemble methods use only a single base learner (also known as homogeneous, e.g., Random Forest (RF)) it is also possible to combine different learning algorithms (also known as heterogeneous) [7], often leading to better results [8]. Various approaches relying on ML for OFP have already been proposed [5], and the use of homogeneous ensembles has already shown promising results in our previous work [9]. However, for OFP, the combination of different learners has not yet been explored.…”

Section: Introductionmentioning

confidence: 99%

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Improving Failure Prediction by Ensembling the Decisions of Machine Learning Models: A Case Study

2019

Self Cite

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The complexity of software has grown considerably in recent years, making it nearly impossible to detect all faults before pushing to production. Such faults can ultimately lead to failures at runtime. Recent works have shown that using Machine Learning (ML) algorithms it is possible to create models that can accurately predict such failures. At the same time, methods that combine several independent learners (i.e., ensembles) have proved to outperform individual models in various problems. While some well-known ensemble algorithms (e.g Bagging) use the same base learners (i.e., homogeneous), using different algorithms (i.e., heterogeneous) may exploit the different biases of each algorithm. However, this is not a trivial task, as it requires finding and choosing the most adequate base learners and methods to combine their outputs. This paper presents a case study on using several ML techniques to create heterogeneous ensembles for Online Failure Prediction (OFP). More precisely, it attempts to assess the viability of combining different learners to improve performance and to understand how different combination techniques influence the results. The paper also explores whether the interactions between learners can be studied and leveraged. The results suggest that the combination of certain learners and techniques, not necessarily individually the best, can improve the overall ability to predict failures. Additionally, studying the synergies in the best ensembles provides interesting insights into why some are able to perform better.

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Exploratory Study of Machine Learning Techniques for Supporting Failure Prediction

Cited by 15 publications

References 20 publications

Performance Comparison of Machine Learning Algorithms for Predictive Maintenance

Performance Comparison of Machine Learning Algorithms for Predictive Maintenance

Robust Anomaly Detection on Unreliable Data

Improving Failure Prediction by Ensembling the Decisions of Machine Learning Models: A Case Study

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