Abstract
In modern Intelligent Maintenance Systems, the machine or equipment robustness also depends on its capability to automatically generate reliability and safety reports. This paper describes an approach to autonomously identify if a faulty signal report has been correctly classified. The proposed approach builds on our previous experience in developing embedded intelligent maintenance systems and helps in avoiding the occurrence of “false positive” interpretations, which means, when the maintenance system indicates a possible fault that does not occur. This index would be useful for real-time monitoring and evaluation on fault detection systems, taking into account several degradation model characteristics. In order to validate the proposed methodology a test bench was developed in a lab reproducing some common faults and degradation processes that may occur in the field. The proposed approach makes use of a data acquisition equipment to store information from sensors to monitor specific physical variables from mechanical components such as gears. A test sequence is applied to the valve control actuators with the following steps: a few seconds of faulty free operational cycle sensor data (which means the opening and closing operations are executing without failure) are collected and then a faulty system behavior is emulated changing some mechanical actuator parts to faulty ones. In the faulty emulation case, a malfunction event must be identified and reported by the fault detection system. The preliminary results indicate that the index is extremely useful especially when the degradation stage of a system is below, for example, catastrophic failure or a predefined level.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lee J (1995) Machine performance monitoring and proactive maintenance in computer-integrated manufacturing: review and perspective. Int J Comput Integr Manuf 8(5):370–380
Pereira CE and Carro L (2007) Distributed real-time embedded systems: recent advances, future trends and their impact on manufacturing plant control. Annu Rev Control 31(1):81–92
Coester (2001) Coester Automation, Actuator Manual—Line CSR CSR 12- CSR 25 - CSR 50 Integral
Goncalves LF, Bosa JL, Balen TR, Lubaszewski MS, Schneider EL, Henriques RV (2011) Fault detection, diagnosis and prediction in electrical valves using self-organizing maps. J Electron Test 27(4):551–564
Piccoli LB, Henriques RVB, Fabris EE, Schneider EL, Pereira CE (2012) Embedded systems solutions for fault detection and prediction in electrical valves, in World congress on engineering asset management (WCEAM), out 2012
Lee J, Qiu H, Ni J, Ad Djurdjanovic D (2004) Infotronics Technologies and Predictive Tools for Next-Generation Maintenance Systems. 11th IFAC INCOM 2004. Salvador/Brazil
Murmu G, Nath R, Convergence performance comparison of transform domain lms adaptive filters for correlated signal, in devices and communications (ICDeCom), 2011 International Conference on, Feb 2011, pp 1–5
Konezny M, Rao S (1995) Improving the dwt-lms algorithm: boundary filter dwt matrix construction, signals, systems and computers. 1995 conferences Record of the twenty-ninth Asilomar conference on, vol 1, pp 75–81
Piccoli LB, Guimarães CSS Jr, Henriques RVB, Winter JM, Muller I, Netto JC, Pereira CE (2013) Embedded fault detection system using wirelessHART networks. NAVCOMP 1:1
Architectures RL, Practices D (2009) Compactrio developers guide, System
Sumathi S, Surekha P (2007) LabVIEW based advanced instrumentation systems, Springer Verlag
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Bisch Piccoli, L., Ventura Bayan Henriques, R., Rocha, C., Ericson Fabris, E., Pereira, C. (2015). Proposal of a Quality Index Applied to Fault Detection Method in Electrical Valves. In: Tse, P., Mathew, J., Wong, K., Lam, R., Ko, C. (eds) Engineering Asset Management - Systems, Professional Practices and Certification. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-09507-3_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-09507-3_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09506-6
Online ISBN: 978-3-319-09507-3
eBook Packages: EngineeringEngineering (R0)