Our research

C-MORE's research area is centred around evidence-based asset management, and takes many forms. Our broad topic areas include condition-based maintenance, asset management, and machine learning approaches for reliability and maintenance.

Condition-based maintenance projects

Condition-based maintenance (CBM) problems combine the age of an asset along with measurement information collected to make optimal decisions on equipment replacement. Good candidates for CBM are medium-sized fleets (10 or more) of large assets with major components that undergo renewal or replacement.

truck

Gold mining haul truck engine replacement problem

In this project, 15 haul truck engine histories were collected over 6 years. There were 8 failures recorded and 13 preventive engine replacements. Using the historical data in concert with oil analysis results, it was determined that measurements of silicon and molybdenum were statistically significant predictors of engine health.

With an estimated preventive replacement cost of $380,000 and an average failure replacement cost of $925,000, the optimal age-based policy was found to be around 18,800 operating hours, whereas the current policy appears to be around 16,000 hours. By simply increasing the cycle length between preventive replacements, a savings of 12.5% could be made, from $44.2/hour to $38.6/hour.

With the use of the condition-based policy, a savings of 15% could be made. By retroactively applying the CBM model to historical records, 3 out of the 8 failures would have been saved, and 11 out of 13 preventive maintenance replacements would have operated additional hours prior to removal.

A simple condition-monitoring solution with big results

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Morbi at viverra diam, vehicula eleifend ex. Nam varius ut orci id bibendum. Phasellus non metus magna. Nunc odio lacus, fermentum sit amet augue quis, vestibulum facilisis neque. Duis nec lectus in orci lobortis consequat. Pellentesque feugiat pretium sollicitudin. Vestibulum imperdiet erat sapien, nec laoreet felis semper a. Nulla eget aliquet mi. Morbi ex dolor, sollicitudin non ex in, hendrerit mattis nunc. Nulla luctus ligula et magna ornare ullamcorper. Nam rhoncus ex eget faucibus semper. Integer dui mauris, elementum in purus a, porta hendrerit justo. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Integer aliquam luctus mollis. Donec lobortis nisi id lacus maximus consectetur. In sagittis consequat dui id venenatis.

 

hkmtr

Asset management projects

The projects in this section pertain to strategies for asset management. These problems include capital asset replacement decisions, spares management decisions, and other problems related to maintenance and asset management. A broad range of problems can fall under this umbrella.

Coal mining company haul truck life cycle cost analysis

At a coal mining company, their fleet of haul trucks were being replaced at a schedule of 13 years. However, due to aging, the trucks required increasing maintenance over time, resulting in decreasing availability for operations. Each truck had a purchase price of 5M USD, and a retiring salvage value of $200,000. By analysis of value of capital assets over time, the recommended replacement period was 8 years. As a result of the increased value at salvage, a savings of $1M per truck could be realized.

blastfurnace

A business case for storing critical spare parts

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Morbi at viverra diam, vehicula eleifend ex. Nam varius ut orci id bibendum. Phasellus non metus magna. Nunc odio lacus, fermentum sit amet augue quis, vestibulum facilisis neque. Duis nec lectus in orci lobortis consequat. Pellentesque feugiat pretium sollicitudin. Vestibulum imperdiet erat sapien, nec laoreet felis semper a. Nulla eget aliquet mi. Morbi ex dolor, sollicitudin non ex in, hendrerit mattis nunc. Nulla luctus ligula et magna ornare ullamcorper. Nam rhoncus ex eget faucibus semper. Integer dui mauris, elementum in purus a, porta hendrerit justo. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Integer aliquam luctus mollis. Donec lobortis nisi id lacus maximus consectetur. In sagittis consequat dui id venenatis.

 

Machine learning projects

Machine learning (ML) is an approach to solving problems where the relationship between the inputs and the outputs are not defined by the user, but discovered by the computer program. It is very powerful because it enables the discovery of hidden patterns in our data that were previously unknown.

In the context of maintenance and asset management, applications of ML may include fleet management strategies, maintenance and sensor data analytics, and more.

Degradation modelling with Bayesian neural networks

When monitoring the health of an asset, some equipment fall under the category of those subject to gradual degradation. That is, rather than a binary operational/failed mode, the equipment gradually loses its functionality over time, and whether it is still operational must be determined by the user. Scenarios such as batteries losing its maximum charge over time, gradual wear of a bearing, or crack propagation are examples of gradual degradation.

In cases such as these, it is beneficial to be able predict when the degradation will cross the pre-set threshold. By combining an auto-encoder, a special type of deep neural network, with the traditional Wiener process-based degradation model to predict the degradation process trajectory and variability, the remaining useful life (RUL) of equipment can be more accurately predicted. This approach was tested on degradation history of 100 turbofan
engines from NASA. C-MORE’s approach of combining the two methods was found to most closely estimate the remaining useful life.

RUL

Local transit light rail track anomaly detection problem

This project arose from hours of infrared video being collected by a local transit company as a consequence of a previous rail failure. This type of rail failure was preemptively identifiable by hot-spots in the rail. By attaching an infrared camera to one of the trains, many hours of condition monitoring data was collected in the form of video.

In order to identify any problems, the task of reviewing the video remained. Rather than leaving it for human review, a machine learning algorithm was applied to the images to identify those that contained anomalies. A combination of  Microsoft Visual Object Tagging Tool and TensorFlow Object Detection API were used to develop and train the algorithm.

anomaly