Fault Detection and Diagnosis in Nonlinear Systems, with a Focus on Mining Truck Suspension Strut

• Author / Creator

  Hajizadeh, Mohammad

• Classical fault detection methods do not completely satisfy the reliability requirement for complex and highly nonlinear stochastic systems. One solution to this problem is to use more advances fault detection methods such as multiple models to simulate system in different operating conditions.
  This study focuses on fault detection and identification (FDI) of suspension strut and particle filter is used as estimator in interacting-multiple-model-based (IMM-based) structure. The main idea of the IMM-based diagnosis algorithm is that the actual system is assumed to have uncertain (failure status) parameter vector affecting the matrices defining the structure of the model. Then, a model set is defined to model each of these different parameters and each model is in certain probability drawn from model set. By calculating these probabilities one can determine the mode in effect at each sampling time and perform fault detection and diagnosis and determine the presence of a particular failure mode.

• Subjects / Keywords

  • Interacting Multiple Model
  • Fault Diagnosis
  • Particle filters
  • Suspension Strut
  • Fault Detection

• Graduation date

  Spring 2014

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3Q96R

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.