The Community for Technology Leaders
RSS Icon
Issue No.10 - Oct. (2012 vol.23)
pp: 1831-1843
Arshdeep Bahga , Georgia Institute of Technology, Atlanta
Vijay K. Madisetti , Georgia Institute of Technology, Atlanta
We present a novel framework, CloudView, for storage, processing and analysis of massive machine maintenance data, collected from a large number of sensors embedded in industrial machines, in a cloud computing environment. This paper describes the architecture, design, and implementation of CloudView, and how the proposed framework leverages the parallel computing capability of a computing cloud based on a large-scale distributed batch processing infrastructure that is built of commodity hardware. A case-based reasoning (CBR) approach is adopted for machine fault prediction, where the past cases of failure from a large number of machines are collected in a cloud. A case-base of past cases of failure is created using the global information obtained from a large number of machines. CloudView facilitates organization of sensor data and creation of case-base with global information. Case-base creation jobs are formulated using the MapReduce parallel data processing model. CloudView captures the failure cases across a large number of machines and shares the failure information with a number of local nodes in the form of case-base updates that occur in a time scale of every few hours. At local nodes, the real-time sensor data from a group of machines in the same facility/plant is continuously matched to the cases from the case-base for predicting the incipient faults—this local processing takes a much shorter time of a few seconds. The case-base is updated regularly (in the time scale of a few hours) on the cloud to include new cases of failure, and these case-base updates are pushed from CloudView to the local nodes. Experimental measurements show that fault predictions can be done in real-time (on a timescale of seconds) at the local nodes and massive machine data analysis for case-base creation and updating can be done on a timescale of minutes in the cloud. Our approach, in addition to being the first reported use of the cloud architecture for maintenance data storage, processing and analysis, also evaluates several possible cloud-based architectures that leverage the advantages of the parallel computing capabilities of the cloud to make local decisions with global information efficiently, while avoiding potential data bottlenecks that can occur in getting the maintenance data in and out of the cloud.
Sensors, Real time systems, Data analysis, Reliability, Cloud computing, Wind turbines, Maintenance engineering, MapReduce, Fault prediction, machine data analysis, case-based reasoning, cloud computing, Hadoop
Arshdeep Bahga, Vijay K. Madisetti, "Analyzing Massive Machine Maintenance Data in a Computing Cloud", IEEE Transactions on Parallel & Distributed Systems, vol.23, no. 10, pp. 1831-1843, Oct. 2012, doi:10.1109/TPDS.2011.306
[1] R.R. Hill, J.A. Stinebaugh, D. Briand, A.S. BenjaminDr., and J. Linsday, "Wind Turbine Reliability: A Database and Analysis Approach," Sandia Report, Feb. 2008.
[2] S. Baumik, "Failure of Turbine Rotor Blisk of an Aircraft Engine," Eng. Failure Analysis, vol. 9, pp. 287-301, 2002.
[3] F.J.G. Carazas and G.F.M. de Souza, "Availability Analysis of Gas Turbines Used in Power Plants," Int'l J. Thermodynamics, vol. 12, no. 1, pp. 28-37, Mar. 2009.
[4] A. Aamodt and E. Plaza, "Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches," AI Comm., vol. 7, pp. 39-59, 1994.
[5] J. Kolodner, Case-Based Reasoning. Morgan Kaufmann, 1993.
[6] R.L. De Mantaras et al., "Retrieval, Reuse, Revision, and Retention in Case Based Reasoning," Knowledge Eng. Rev., vol. 20, pp. 215-240, 2005.
[7] S.G. Lee and Y.C. Ng, "Hybrid Case-Based Reasoning for On-Line Product Fault Diagnosis," Int'l J. Advanced Manufacturing Technology, vol. 27, pp. 833-840, 2005.
[8] T.M. Khoshgoftaar, K. Ganesan, E.B. Allen, F.D. Ross, R. Munikoti, N. Goel, and A. Nandi, "Predicting Fault-Prone Modules with Case-Based Reasoning," Proc. Eighth Int'l Symp. Software Reliability Eng., 1997.
[9] N. Zhong, J. Dong, and S. Ohsuga, "Using Rough Sets with Heuristics for Feature Selection," J. Intelligent Information Systems, vol. 16, no. 3, pp. 199-214, 2001.
[10] Z. Pawlak, "Rough Sets," Int'l J. Computer and Information Sciences, vol. 11, pp. 341-356, 1982.
[11] M. Devaney and B. Cheetham, "Case-Based Reasoning for Gas Turbine Diagnostics," Proc. 18th Int'l FLAIRS Conf., 2005.
[12] H. Timmerman, "SKF WindCon Condition Monitoring System for Wind Turbines," Proc. New Zealand Wind Energy Conf., 2009.
[13] S. Markovich and P. Scott, "The Role of Forgetting in Learning," Proc. Fifth Int'l Conf. Machine Learning, vol. 5, pp. 459-465, 1988.
[14] C. Yang, R. Orchard, B. Farley, and M. Zaluski, "Automated Case Base Creation and Management," Proc. 16th Int'l Conf. Developments in Applied Artificial Intelligence (IEA/AIE), 2003.
[15] B. Smyth, "Case-Based Maintenance," Proc. 11th Int'l Conf. Industry and Eng. Applications of AI and Expert Systems, 1998.
[16] B. Smyth, "Remembering to Forget: A Competence Persevering Deletion Policy for Case-Based Reasoning Systems," Proc. 14th Int'l Joint Conf. AI, pp. 377-382, 1995.
[17] J. Zhu and Q. Yang, "Remembering to Add: Competence Persevering Case-Addition Policy for Case-Base Maintenance," Proc. 16th Int'l Joint Conf. AI, pp. 234-239, 1999.
[18] Z. Pawlak, "Rough Sets and Decision Analysis," Information Sciences, vol. 38, no. 3, pp. 132-144, 2000.
[19] S. Ji, S.-F. Yuan, and S.-P. Wang, "An Algorithm for Case-Based Reasoning Based on Similarity Rough Set," Proc. Fifth Int'l Conf. Fuzzy Systems and Knowledge Discovery, 2008.
[20] B. Smyth and P. Cunningham, "The Utility Problem Analysed—A Case-Based Reasoning Perspective," Proc. Third European Workshop Case-Based Reasoning (EWCBR), 1996.
[21] K. Kalpakis and S. Tang, "Collaborative Data Gathering in Wireless Sensor Networks Using Measurement Co-Occurrence," Computer Comm., vol. 31, no. 10, pp. 1979-1992, June 2008.
[22] Apache Hadoop, http:/, 2012.
[23] Pig, http://hadoop.apache.orgpig, 2012.
31 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool