# Condition-Based Monitoring and Maintenance: State of the Art Review
## 3 University Research Related to CBM
At the University of Toronto, Canada [156], the research and development in the Vibration Monitoring, Signal Processing and CBM Laboratory focuses on the development of effective fault detection and diagnostic schemes on the basis of analysis and modelling of vibration data for CBM purposes. Moreover, CBM software developed at the University of Toronto, EXAKT, is quite well known in the industry for maintenance optimization. Furthermore, at their Centre for Maintenance Optimization and Reliability Engineering (C-MORE), they employ proportional hazards modelling to pinpoint the risk factors that threaten the health of the asset from all signals obtained during health monitoring. This hazard estimate (the conditional probability of failure) is then blended with economic considerations to establish optimal CBM decisions. A large amount of research on maintenance, predictive failure, e-manufacturing, and industry applications with embedded system are conducted at the NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems with multi-campus collaboration (University of Cincinnati, University of Michigan-Ann Arbor, and Missouri University of Science and Technology). At the Robotics Research Group at the University of Texas at Austin [157], researchers are working on a method for automatic condition-based maintenance (CBM) that is based on decision-making criteria. This is to enhance the reliability, safety, and maintainability of robot actuators or other variable duty cycle machines and to reduce the cost of their overall maintenance. They are currently developing a decision making CBM (DM/CBM) to overcome the problems of modern model-based CBM. They have created a software test that plays an important part in the development of decision-making software that also will be used as a guideline for future students who will perform CBM-related research. At the Centre for Operational Research and Applied Statistics (CORAS) of the University of Salford, Greater Manchester University, United Kingdom, the research team is working on CBM [158], having concentrated their attention to date on developing models of the decision aspect of condition monitoring with the aim of optimizing a criterion function of interest. They have used stochastic filtering and hidden Markov models to model predictions of the residual life of monitored engineering systems, and hence to provide cost effective decision support. The conditional residual life formulation using semi-deterministic stochastic filtering is perhaps the first of this kind in condition-based maintenance modelling, which has been followed by many others since. The research has closely collaborated with industry users and engineering departments at other universities [159]. Other than the above-mentioned universities, the following is a list of a few universities wherein over the past few years, CBM/reliability-related work has been conducted:  
1. Intelligent Systems Laboratory, Department of Mechanical, Industrial, and Nuclear Engineering, University of Cincinnati, Cincinnati, OH, USA. 2. Automation and Robotics Research Institute, Ft. Worth, Texas, USA. 3. Zhejiang University, China. 4. Lulea University of Technology, Sweden. 5. Malardalen University, Sweden. 6. University of Groningen, Groningen, The Netherlands. 7. Wichita State University, USA. 8. National Institute of Technology, Tiruchirappalli, India. 9. Reliability Engineering Centre, Indian Institute of Technology, Kharagpur, West Bengal, India. 10. Department of Agricultural Machinery, Faculty of Biosystems Engineering, University of Tehran, Iran. 11. Vaxjo University, School of Technology and Design, Sweden. 12. School of engineering, Cranfield University, Cranfield, England, United Kingdom. 13. Division of Industrial and Information Systems Engineering, Ajou University, South Korea. 14. Queensland University of Technology, Brisbane, Queensland, Australia. 15. Federal University of Rio Grande do Norte, Rio Grande do Norte, Brazil. 16. University of Teside, Middlesbrough, United Kingdom. 17.