Condition Monitoring in a Hydraulic System of an Industrial Machine Using Unscented Kalman Filter

Razavi, Behnam; Silva, Clarence W. de

doi:10.1142/s0219878910002191

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…This research is based on Evolutionary Design System (figure 1.1) proposed by [ and further enhanced in [Gamage and de Silva, 2010;de Silva 2010]. In this framework, there is an industrial machine connected to a health monitoring system which can identify faults and detect malfunctions [Raman and de Silva, 2009;Razavi and de Silva, 2010]. These faults or performance degradation can be due to wear and tear or design weaknesses of the industrial machine.…”

Section: Evolutionary Design Frameworkmentioning

confidence: 99%

Design evolution of engineering systems using bond graphs and genetic programming

2016

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Engineering design is a complex task, which typically involves multiple physical domains. It can benefit from a modeling tool that can represent different domains in a unified manner. For complex designs, optimization by traditional techniques (such as gradient-based methods) may not be appropriate. Evolutionary methodologies may be used in design optimization of complex engineering systems. This research is based on a framework for evolutionary design system consisting of a machine health monitoring system, model-based evolutionary design optimization system, and a design expert system. The design weaknesses and faults of an existing engineering system are identified using a machine health monitoring system. Then under supervision of the design expert system, an optimal design is evolved using genetic programming. This thesis primarily addresses the modeling and evolutionary aspects and their integration. The thesis develops the integrated system consisting of bond-graph modeling and genetic programming. The performance of the developed system is studied using both experimentation and simulation. The drawbacks of the fitness calculation methodologies that are presented in literature are identified and improved fitness functions are developed in the present work. A methodology to automatically obtain the state-space model of a system represented using bond graphs is also developed. While previous researchers have investigated the integration of bond graphs and genetic programming in design, they have not applied the method in a real engineering system. The present work specifically addresses the application of the developed method for design improvement for an industrial machine. For this purpose a linear bond graph model of the industrial fish processing machine is developed and the parameter values are identified using genetic programming. The design of the actual system is modified according to the evolved bond graph model and the results are validated using the data from the actual engineering iii system. The proposed method is applicable particularly to actual systems, first because the initial model can be tested by comparing its simulated results with the corresponding results from the actual system, and second because the design improvements as suggested by the evolutionary design framework may be implemented and tested against the behaviour of the corresponding model.

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Section: Evolutionary Design Frameworkmentioning

confidence: 99%

Design evolution of engineering systems using bond graphs and genetic programming

2016

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“…Studies in the literature are even directed towards state and parameter estimations of heavy machinery [42]. The UKF has been employed for online monitoring and fault diagnosing in complex hydraulic systems consisting of hydraulic actuators and the servovalve spool [43]. In the framework of multibody system dynamics, studies have focused on using the error-state EKF to estimate the states of simple hydraulic mechanisms [44], the augmented EKF to estimate the parameters of simple hydraulic mechanisms [45], and the UKF to estimate the states of a forestry crane using the sensor measurements from the simulation models [46].…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation into the State Estimation of a Forestry Crane Using the Unscented Kalman Filter and a Multiphysics Model

et al. 2022

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“…Statistical Process Control (SPC) 14 is the typical tool, as proposed by Liu et al 15 and Colosimo et al 16 Diagnosis is a classification problem. Several types of algorithms could be used to localise and identify the nature of the fault: Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), 17 Mahalanobis-Taguchi Systems, 18 filtering techniques, for example, Unscented Kalman Filter (UKF) 19 and Artificial Neural Networks (ANN) are just a subset of possible solutions. 11,20,21 An innovation in this field could be progressive learning, introducing the capability of increasing the number of clusters during online learning.…”

Section: Introductionmentioning

confidence: 99%

A novel prognostics solution for machine tool sub-units: The hydraulic case

Bernini

Waltz

Albertelli

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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A novel prognostic approach was developed and applied to a machine tool hydraulic unit. Three components were considered: pump, sensor and valve. The proposed methodology exploited a digital twin of the system to perform simulations of the healthy and faulty machine. The digital twin was properly validated through experiments. This approach dealt with the need to carry out time-consuming and expensive experimental campaigns, that is, run-to-failures – not affordable in many industrial applications. The diagnosis module was trained on digital twin simulations and fulfilled the fault detection, isolation and quantification phases. The challenge related to the variability of the operating conditions of the machine was addressed through a robustness analysis of the methodology. The solution successfully dealt with both stationary and non-stationary working conditions. A dedicated classification model was designed for each faulty component, maximising the associated classification rate. The testing procedure consisted of the application of a 10-fold cross-validation to compute the mean classification rates for stationary and non-stationary working conditions. Diagnosis performance results were excellent for the pump, whereas they were lower for the sensor and valve, reaching 79.75% and 74.93% accuracy respectively for the most challenging working cycle. The prognosis directly exploited the output of diagnostics, allowing for experimental effort reduction. Prognosis predictions were built starting from the updated health status provided by the diagnosis output. In order to test the prognosis module, mean and standard deviation of the prediction errors (less than 1.176%) were computed through a Monte Carlo approach. The conceived methodology allowed one of the critical goals of prognostics to be handled: the Remaining Useful Life probability density function estimation.

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Condition Monitoring in a Hydraulic System of an Industrial Machine Using Unscented Kalman Filter

Cited by 7 publications

References 22 publications

Design evolution of engineering systems using bond graphs and genetic programming

Design evolution of engineering systems using bond graphs and genetic programming

Experimental Investigation into the State Estimation of a Forestry Crane Using the Unscented Kalman Filter and a Multiphysics Model

A novel prognostics solution for machine tool sub-units: The hydraulic case

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