The Experts below are selected from a list of 27084 Experts worldwide ranked by ideXlab platform
Ahm Ter Hofstede - One of the best experts on this subject based on the ideXlab platform.
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Root Cause Analysis with enriched process logs
Business Process Management, 2012Co-Authors: Suriadi Suriadi, Chun Ouyang, Wmp Wil Van Der Aalst, Ahm Ter HofstedeAbstract:In the field of process mining, the use of event logs for the purpose of Root Cause Analysis is increasingly studied. In such an Analysis, the availability of attributes/features that may explain the Root Cause of some phenomena is crucial. Currently, the process of obtaining these attributes from raw event logs is performed more or less on a case-by-case basis: there is still a lack of generalized systematic approach that captures this process. This paper proposes a systematic approach to enrich and transform event logs in order to obtain the required attributes for Root Cause Analysis using classical data mining techniques, the classification techniques. This approach is formalized and its applicability has been validated using both self-generated and publicly-available logs.
Soumik Sarkar - One of the best experts on this subject based on the ideXlab platform.
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Root-Cause Analysis for Time-series Anomalies via Spatiotemporal Graphical Modeling in Distributed Complex Systems
arXiv: Machine Learning, 2018Co-Authors: Chao Liu, Kin Gwn Lore, Zhanhong Jiang, Soumik SarkarAbstract:Performance monitoring, anomaly detection, and Root-Cause Analysis in complex cyber-physical systems (CPSs) are often highly intractable due to widely diverse operational modes, disparate data types, and complex fault propagation mechanisms. This paper presents a new data-driven framework for Root-Cause Analysis, based on a spatiotemporal graphical modeling approach built on the concept of symbolic dynamics for discovering and representing causal interactions among sub-systems of complex CPSs. We formulate the Root-Cause Analysis problem as a minimization problem via the proposed inference based metric and present two approximate approaches for Root-Cause Analysis, namely the sequential state switching ($S^3$, based on free energy concept of a restricted Boltzmann machine, RBM) and artificial anomaly association ($A^3$, a classification framework using deep neural networks, DNN). Synthetic data from cases with failed pattern(s) and anomalous node(s) are simulated to validate the proposed approaches. Real dataset based on Tennessee Eastman process (TEP) is also used for comparison with other approaches. The results show that: (1) $S^3$ and $A^3$ approaches can obtain high accuracy in Root-Cause Analysis under both pattern-based and node-based fault scenarios, in addition to successfully handling multiple nominal operating modes, (2) the proposed tool-chain is shown to be scalable while maintaining high accuracy, and (3) the proposed framework is robust and adaptive in different fault conditions and performs better in comparison with the state-of-the-art methods.
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Root-Cause Analysis for time-series anomalies via spatiotemporal causal graphical modeling.
arXiv: Learning, 2016Co-Authors: Chao Liu, Kin Gwn Lore, Soumik SarkarAbstract:Modern distributed cyber-physical systems encounter a large variety of anomalies and in many cases, they are vulnerable to catastrophic fault propagation scenarios due to strong connectivity among the sub-systems. In this regard, Root-Cause Analysis becomes highly intractable due to complex fault propagation mechanisms in combination with diverse operating modes. This paper presents a new data-driven framework for Root-Cause Analysis for addressing such issues. The framework is based on a spatiotemporal feature extraction scheme for multivariate time series built on the concept of symbolic dynamics for discovering and representing causal interactions among subsystems of a complex system. We propose sequential state switching ($S^3$) and artificial anomaly association ($A^3$) methods to implement Root-Cause Analysis in an unsupervised and semi-supervised manner respectively. Synthetic data from cases with failed pattern(s) and anomalous node are simulated to validate the proposed approaches, then compared with the performance of vector autoregressive (VAR) model-based Root-Cause Analysis. The results show that: (1) $S^3$ and $A^3$ approaches can obtain high accuracy in Root-Cause Analysis and successfully handle multiple nominal operation modes, and (2) the proposed tool-chain is shown to be scalable while maintaining high accuracy.
Wmp Wil Van Der Aalst - One of the best experts on this subject based on the ideXlab platform.
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Business Process Management Workshops - Root Cause Analysis with Enriched Process Logs
Business Process Management Workshops, 2013Co-Authors: Suriadi Suriadi, Chun Ouyang, Wmp Wil Van Der Aalst, Ahm Arthur Ter HofstedeAbstract:In the field of process mining, the use of event logs for the purpose of Root Cause Analysis is increasingly studied. In such an Analysis, the availability of attributes/features that may explain the Root Cause of some phenomena is crucial. Currently, the process of obtaining these attributes from raw event logs is performed more or less on a case-by-case basis: there is still a lack of generalized systematic approach that captures this process. This paper proposes a systematic approach to enrich and transform event logs in order to obtain the required attributes for Root Cause Analysis using classical data mining techniques, the classification techniques. This approach is formalized and its applicability has been validated using both self-generated and publicly-available logs.
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Root Cause Analysis with enriched process logs
Business Process Management, 2012Co-Authors: Suriadi Suriadi, Chun Ouyang, Wmp Wil Van Der Aalst, Ahm Ter HofstedeAbstract:In the field of process mining, the use of event logs for the purpose of Root Cause Analysis is increasingly studied. In such an Analysis, the availability of attributes/features that may explain the Root Cause of some phenomena is crucial. Currently, the process of obtaining these attributes from raw event logs is performed more or less on a case-by-case basis: there is still a lack of generalized systematic approach that captures this process. This paper proposes a systematic approach to enrich and transform event logs in order to obtain the required attributes for Root Cause Analysis using classical data mining techniques, the classification techniques. This approach is formalized and its applicability has been validated using both self-generated and publicly-available logs.
Suriadi Suriadi - One of the best experts on this subject based on the ideXlab platform.
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Business Process Management Workshops - Root Cause Analysis with Enriched Process Logs
Business Process Management Workshops, 2013Co-Authors: Suriadi Suriadi, Chun Ouyang, Wmp Wil Van Der Aalst, Ahm Arthur Ter HofstedeAbstract:In the field of process mining, the use of event logs for the purpose of Root Cause Analysis is increasingly studied. In such an Analysis, the availability of attributes/features that may explain the Root Cause of some phenomena is crucial. Currently, the process of obtaining these attributes from raw event logs is performed more or less on a case-by-case basis: there is still a lack of generalized systematic approach that captures this process. This paper proposes a systematic approach to enrich and transform event logs in order to obtain the required attributes for Root Cause Analysis using classical data mining techniques, the classification techniques. This approach is formalized and its applicability has been validated using both self-generated and publicly-available logs.
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Root Cause Analysis with enriched process logs
Business Process Management, 2012Co-Authors: Suriadi Suriadi, Chun Ouyang, Wmp Wil Van Der Aalst, Ahm Ter HofstedeAbstract:In the field of process mining, the use of event logs for the purpose of Root Cause Analysis is increasingly studied. In such an Analysis, the availability of attributes/features that may explain the Root Cause of some phenomena is crucial. Currently, the process of obtaining these attributes from raw event logs is performed more or less on a case-by-case basis: there is still a lack of generalized systematic approach that captures this process. This paper proposes a systematic approach to enrich and transform event logs in order to obtain the required attributes for Root Cause Analysis using classical data mining techniques, the classification techniques. This approach is formalized and its applicability has been validated using both self-generated and publicly-available logs.
Harri Kytömaa - One of the best experts on this subject based on the ideXlab platform.
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Root Cause Analysis of a Gas Turbine Compressor Stator Blade Failure
ASME 2005 Power Conference, 2005Co-Authors: Filippo Gavelli, Jude R. Foulds, Robert A. Sire, Harri KytömaaAbstract:Three identical 85 MW combustion turbines experienced cracking and failure of several first stage compressor stator (S1) blades. The Root Cause Analysis involved the following distinct stages: 1. Examination of failed blades including fractography and analyses of the mechanics of their fracture; 2. Blade vibration Analysis to determine the modes of vibration and corresponding resonant frequencies; 3. Inlet plenum flow and acoustical Analysis to identify possible sources of excitation. This paper summarizes the methodology and results of the Root Cause Analysis investigation and outlines the convergence of results obtained from the independently conducted mechanical and acoustical analyses.Copyright © 2005 by ASME
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Root Cause Analysis of a Gas Turbine Compressor Stator Blade Failure
ASME 2005 Power Conference, 2005Co-Authors: Filippo Gavelli, Jude R. Foulds, Robert A. Sire, Harri KytömaaAbstract:Three identical 85 MW combustion turbines experienced cracking and failure of several first stage compressor stator (S1) blades. The Root Cause Analysis involved the following distinct stages: 1. Examination of failed blades including fractography and analyses of the mechanics of their fracture; 2. Blade vibration Analysis to determine the modes of vibration and corresponding resonant frequencies; 3. Inlet plenum flow and acoustical Analysis to identify possible sources of excitation. This paper summarizes the methodology and results of the Root Cause Analysis investigation and outlines the convergence of results obtained from the independently conducted mechanical and acoustical analyses.
