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Permanent link (DOI): https://doi.org/10.7939/R3J67949D

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System Level Monitoring for Time-Varying Conditions with Application to Ground Engaging Equipment Open Access

Descriptions

Other title
Subject/Keyword
Shovel
Signal processing
Soil-Tool interaction
Excavation
Soil property estimation
Equipment Health Monitoring
Earthmoving equipment
Ground engaging equipment
Fault detection
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Yousefi, Nima
Supervisor and department
Michael G. Lipsett (Mechanical Engineering)
Examining committee member and department
Martin Barczyk (Mechanical Engineering)
Michael G. Lipsett (Mechanical Engineering)
Robert Hall (Petroleum and Mining Engineering)
Laeeque Daneshmend (Mining Engineering - Queen's University)
Ming J. Zuo (Mechanical Engineering)
Department
Department of Mechanical Engineering
Specialization

Date accepted
2015-09-22T14:57:56Z
Graduation date
2015-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Condition monitoring is an effective tool for protecting equipment against unplanned and costly downtime. Conventionally, condition monitoring is only focused on the state of equipment's health and tracks the internal changes. However, more recent studies pointed out that an effective monitoring system not only should track the changes occurring at the equipment level, but it should also take the variability in the equipment operational environment into account. In this approach, environmental variability, along with the load and speed changes are considered as operational variations. These external changes, similar to the internal ones, trigger a non-stationary operating envelope, and therefore need to be monitored at a system level. Shovels and wind turbines are primary examples of systems exposed to such conditions. In this context, the present work aims to advance the body of knowledge in the area of system level monitoring for earthmoving equipment, such as ground engaging equipment. System level monitoring presented in this study entails assessment of environmental properties as well as equipment condition, and it is addressed in two steps. An algorithm is developed for monitoring and detection of the environmental/external variation. It takes machine-ground interaction force as input, and estimates characteristic features of the ground. The detection of the internal changes (i.e. structural damages) is accomplished through a novel acceleration-based monitoring method. It tracks the changes in the kinetic energy of the system caused by developing structural defect. By performing the internal and external monitoring, it is possible to identify the root cause of the change in the system level. To evaluate these methods, a simplified shovel test rig is designed and fabricated. Using an off-set crank-slider mechanism, the rig can generate a time-varying motion and simulate the tool-ground interaction of a real shovel. The design of the test rig allows for it to be used as a platform to study the behavior of a system that is under speed and load variations. The test rig can perform cutting and pushing through a variety of granular material, in a controlled manner which facilitates the study of the environmental variation. Measuring the soil-tool interaction force and using a tool-ground interaction model the mechanical properties of the medium can be estimated. The test rig is designed such that it enables the replication of various structural damages. Acceleration signal of the slider, that is a representation of the kinetic energy of the system, is recorded and used for the assessment of the condition of the test rig. In order to extract fault signatures from the signal, a variety of signal processing methods in time, frequency and time-frequency domains are used. Analyzing these features, it is found that the acceleration signal carries fault signatures, and all faulty conditions can be distinguished from the healthy ones. Application of advanced signal processing methods such as STFT and HHT, support the proposed approach and suggest that it can be successfully used for both detection and identification of the considered faults. Methods developed for external and internal monitoring provide a platform for equipment situational awareness that can be adopted in other areas such as earthwork planning, excavation automation and equipment health monitoring.
Language
English
DOI
doi:10.7939/R3J67949D
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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