Led by Professor Hal Gurgenci, head of Mechanical Engineering at The University of Queensland, the work is part of CMTE’s contribution to the $4m Australian Coal Association Research Program (ACARP) Longwall Automation project being conducted jointly by CMTE and CSIRO.

In a preliminary scoping study towards this project, Professor Gurgenci’s group analysed various types of failures that occur on longwalls using both historical maintenance records collected over several years and on-line data collected

over a shorter period.

Professor Gurgenci said what was most striking about the results were the number of failure types that not only occurred frequently, but which also regularly took the mine a long time to fix.

“These types of failures indicate deficiencies in the original design of the longwall equipment or in the selection and sizing of the equipment for the particular mining context,” said Gurgenci.

“The fact that these types of failures are occurring at all gives clues as to why longwall downtime averages 40-50%.”

Failures were analysed as acute, acute and chronic, and chronic.

“Even though they’re easily fixed, those failures that occur chronically, are still causing significant amounts of downtime,” said Gurgenci.

“Acute failures are probably caused by a design deficiency and really should have been eliminated early in the use of the longwall equipment,” he said.

“But most surprising is that equipment that is meant to be mature is experiencing both acute and chronic problems.”

“The maintenance strategy for a beast like that is very difficult,” said Gurgenci, “and that’s why our team is working on two fronts.”

Gurgenci said CMTE was developing tools to improve the design and hence reliability of longwall equipment and tools that would better monitor the condition of longwalls.

In the area of design aids, software that will predict the behaviour of Armoured Face Conveyors (AFCs) under different operating scenarios has been developed by CMTE postgraduate student David Wauge (see related article, link below).

Also on the design front is software that will assist in designing the most appropriate configuration for the lacing of picks on a longwall shearer. This software is now complete and a proposal has been submitted to ACARP to test the software in Australian coal seam conditions.

The Pick Lacing Design Software can predict the dynamic forces and vibrations that occur during shearer operation and is able to simulate systems with different lacing patterns under different cutting modes.

In the area of condition monitoring, Gurgenci said the researchers were investigating ways to detect faults as well as tools for monitoring the trends on each longwall.

Two of CMTE’s postgraduate students, Daniel Bongers and Brad Barter, have been developing systems to detect and classify faults as each occurs.

Bongers has developed a system based on neural networks. He played an integral role in the collection and analysis of on-line and historical data; spending 12 weeks at longwall mines in the early stages of his project. With his research now complete, the data-based model has shown to be capable of detecting a selected group of faults with a 90%

success rate.

“Daniel’s research, while it is not the complete solution, provides us with a valuable diagnostic tool for longwalls,” said Gurgenci.

“It uses the expensive sensors already on longwalls which haven’t been used to their full advantage and gives mines a way to be able to tell what fault has occurred when the longwall stops.”

Using a different approach, Brad Barter is developing a model-based fault detection system that compares the dynamic modes of the AFC using a scaled model.

In parallel, CMTE postgraduate student Anthony Reid has been developing an on-line estimator for the forces acting on individual picks. The estimator will infer pick forces using the vibrations and motor currents coming off the shearer.

According to Gurgenci it would be useful in several ways, but most importantly, for horizon control.

“Achieving horizon control on the shearer is one of the first hurdles in longwall automation,” said Gurgenci.

“Our goal is to develop a reliable way of mapping the cutting effort of the shearer against the geometry of the longwall face. That would allow the mine to identify where the shearer was, relative to the coal interface, and as well, to locate discontinuities or hard bands.”

Gurgenci said the estimator would also be capable of monitoring the health of the power transmission to the shearer and the condition of the individual cutting picks.

He said the ‘search’ for horizon control using instrumented picks was not new and that such approaches had failed in the harsh mine environment.

“As well as the issue of robustness of sensors, there was no simple solution for how data from each pick would be transmitted from the outside of the rotating drums to a device on the shearer,” said Gurgenci.

“CMTE is taking a different approach by using a model of the shearer to infer the pick forces from more measurable quantities such as the motor currents, ranging arm vibrations and hydraulic cylinder pressures,” he said.

“Put simply, we’re creating a soft sensor to measure the pick force.”

Gurgenci said these projects represented only a few of the numerous studies ongoing at both CMTE and the CSIRO towards the ACARP Longwall Automation project (See related articles.)