On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine



Published Nov 19, 2020
Elisabeth K¨allstr¨om Tomas Olsson John Lindstr¨om Lars Hakansson Jonas Larsson


In order to reduce unnecessary stops and expensive downtime originating from clutch failure of construction equipment machines; adequate real time sensor data measured on the machine in combination with feature extraction and classification methods may be utilized.
This paper presents a framework with feature extraction methods and an anomaly detection module combined with Case-Based Reasoning (CBR) for on-board clutch slippage detection and diagnosis in heavy duty equipment. The feature extraction methods used are Moving Average Square Value Filtering (MASVF) and a measure of the fourth order statistical properties of the signals implemented as continuous queries over data streams. The anomaly detection module has two components, the Gaussian Mixture Model (GMM) and the Logistics Regression classifier. CBR is a learning approach that classifies faults by creating a new solution for a new fault case from the solution of the previous fault cases. Through use of a data stream management system and continuous queries (CQs), the anomaly detection module continuously waits for a clutch slippage event detected by the feature extraction methods, the query returns a set of features, which activates the anomaly detection module. The first component of the anomaly detection module trains a GMM to extracted features while the second component uses a Logistic Regression classifier for classifying normal and anomalous data. When an anomaly is detected, the Case-Based diagnosis module is activated for fault severity estimation.

Abstract 26 | PDF Downloads 21



Case-Based Reasoning, Fourth Order Statistics, Gaussian Mixture Model, Linear Regression and Moving Average Square Value filtering

Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues. In Ai communications.
Aha, D., Kibler, D., & Albeit, M. (1991). Instance-based learning algorithms, machine learning. In Machine learning.
Andren, L., H°akansson, L., Brandt, A., & Claesson, I. (2004). Identification of dynamic properties of boring bar vibrations in a continuous boring operation. In Mechanical systems and signal processing.
Bendat, J., & Piersol, A. (Eds.). (2010). Random data; analysis and measurement procedures. Wiley.
Berglund, K. (2013). Predicting wet clutch service life performance, (Unpublished doctoral dissertation). Lule°a University of Technology.
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 79-86.
DeCarlo, L. T. (1997). On the meaning and use of kurtosis. In Psychological methods.
Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from incomplete data via the em algorithm. In Journal of the royal statistical society.
Devlin, M., Tersigni, S., Senn, J., Turner, T., Jao, T., & Yatsunami, K. (2004). Effect of friction material on the relative contribution of thin-film friction to overall friction in clutches. In Sae international.
Fatima, N., Marklund, P., & Larsson, R. (2012). Water contamination effect in wet clutch system. In Proceedings of the institute of mechanical engineers, part d, journal of automobile engineering.
Fatima, N., Marklund, P., & Larsson, R. (2013). Influence of clutch output shaft inertia and stiffness on the performance of the wet clutch. Tribology Transactions, 56(2), 310-319.
Kazunari, O., Akihiko, F., & Takeshi, H. (2009). Proposal of field life design method for wet multiple plate clutches of automatic transmission on forklifttrucks. In Sae international.
Kullback, S., & Leibler, R. (1951). On information and sufficiency. The Annals of Mathematics Statistics, 22(1),79-86.
Leake, D., & McSherry, D. (2005). Introduction to the special issue on explanation in case-based reasoning (Vol. 24; Tech. Rep. No. 2). Artificial Intelligence Review,.
Lindstr¨om, J., Plankina, D., Nilsson, K., Parida, V., Ylinen ¨a¨a, H., & Karlsson, L. (2013). Functional products: Business model elements. In Proceedings of 5th cirp international conference on industrial productservice systems.
Lingesten, N. (2012). Wear behavior of wet clutches (Unpublished doctoral dissertation). Lule°a University of Technology.
M¨aki, R. (2005). Wet clutch tribology; friction characteristics in limited slip differentials (Unpublished doctoral dissertation). Lule°a University of Technology.
Manolakis, D., Ingle, V., & Kogon, S. (Eds.). (2000). Statistical and adaptive signal processing. McGraw-Hill.
Marklund, P. (2010). Permeability measurements of sintered and paper based friction materials for wet clutches and brakes. In Sae.
Marx, S., Luck, J., Pitla, S., & Hoy, R. (2016). Comparing various hardware/software solutions and conversion methods for controller area network (can) bus data collection. Computers and Electronics in Agriculture, 128(3), 141-148.
Meier, H., Roy, R., & Seliger, G. (2008). Industrial productservice systems - ips2. CIRP Annals Manufacturing Technology, 1-24.
Murphy, K. (2012). Machine learning: a probalistic perspective (Tech. Rep.). MIT Press.
Olsson, T., Gillblad, D., Funk, P., & Xiong, N. (2014). Explaining probabilistic fault diagnosis and classification using case-based reasoning. In Proceedings of case-based reasoning reasearch and development international conference on case-based reasoning.
Olsson, T., K¨allstr¨om, E., Gillblad, D., Funk, P., Lindstr¨om, J., H°akansson, L., . . . Larsson, J. (2014). Fault diagnosis of heavy duty machines: Automatic transmission clutches. In Proceedings of workshop on synergies between cbr and data mining at 22nd international conference on case-based reasoning.
Ompusunggu, A., Papy, J., Vandenplas, S., Sas, P., & Brussel, H. (2012). Condition monitoring method for automatic transmission clutches. In International journal of prognosis and health management.
Papoulis, A. (Ed.). (1991). Probability, random variables, and stochastic processes. McGraw-Hill.
Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets. PloS one, 10(3), e0118432.
Schwarz, G., et al. (1978). Estimating the dimension of a model. The annals of statistics, 6(2), 461–464.
Setu, M., Wilcutts, M., Chigusa, S., Qiao, L., Choi, K., & Pattipati, K. (2006). Systematic data-driven approach to real-time fault detection and diagnosis in automative engines. In Proceedings of ieee.
Xu, C., Wedlund, D., Helgoson, M., & Risch, T. (2013). Model-based validation of streaming data. In Proceedings of the 7th acm international conference on distributed event-based systems, debs.
Zeitler, E., & Risch, T. (2011). Model-based validation of streaming data. In Proceedings of the vldb endowment.
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