Guan, Z.

Gurgenci, H.

University of Queensland eSpace

RELIABILITY IMPROVEMENT THROUGH SMART LONGWALLS PROJECT 
 
Zhiqiang Guan and Hal Gurgenci 
Introduction 
 
Coal produced by Longwall mining is almost 30% of the total coal production in 
Australia, distributed between NSW and QLD and with production tonnage and face 
numbers as shown in Figure 1. 
 
Traditionally, Australian longwall miners have favoured large equipment to keep the unit 
costs down. While this enabled longwall production to enjoy productivity figures 
approaching those for some open cut mines in Australia, the low availability and 
utilization have made it difficult to extract maximum use from installed capacity. 
 
Typical downtime figures in Australian longwalls are around  60% of the calendar time,  
most of which are associated with face equipment failures. Therefore, a critically 
important element of the Australian drive towards catching up with world’s best practice 
in higher utilization of the installed capacity, is improving face equipment reliability.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig.1  Longwall face number & production in NSW and QLD (Joint Coal Board) 
 
CRC Mining Smart Longwalls Project is aiming to achieve a significant increase in 
longwall productivity by improving the reliability of face equipment to benefit its 
members. The immediate target for reliability improvement of face equipment is to 
quickly identify the failures and their root cause by modeling, date collection and analysis, 
fault detection, and condition monitoring. The long-term target is to eliminate some faults 
by re-design & re-engineering, fault prediction and automatic avoidance of the fault. 
 
 
Common failures in Longwall face equipment 
 
The research priorities in Smart Longwall project have been developed by surveying 
equipment failures in selected sites. A Pareto-style representation of top 50 production 
delays in a survey site is shown in Figure 2.  This figure shows that the top 10 delays 
account for almost 55% of total failures and the top 4 delays alone account for nearly 
30% of the total failures. These points are summarised in Table 1. 
 
 
Fig. 2. Ranking of top 50 longwall face equipment delays 
 
 
Table 1  Top delays in a face equipment 
  
Contribution 
to total face 
equipment 
downtime 
Top 50  ~90% 
Top 10  ~55% 
AFC Overload 
Ranging Arm Gearbox 
AFC Chain Failures Top 4 
BSL Chain Failures 
~30% 
 
 
A better representation of the delay ranking is given by a scatter plot, which plots Mean 
Time To Repair (MTTR) against number of breakdowns.  This is shown in Figure 3. 
  
Figure 3. Scatter Plot for longwall face equipment failures 
 
 
The scatter plot representation, as first suggested by Knights(2001), is a useful tool to 
represent the distribution of the downtime categories in a given equipment system.  In the 
scatter plot, the graph area is separated into four quadrants. The lower left quadrant 
includes those failures that do not appear too often and are easily fixed. Attention should 
be focussed on the other three quadrants: Acute; Chronic; and Acute & Chronic. Among 
the top 4 delays being identified, the top 1 delay (AFC Overload as marked 3 in Fig.3) is 
a chronic problem and the top 2 to top 4 delays (Ranging arm gearbox, AFC and BSL 
chain failures as marked 44, 178 & 179 in Fig.3) are acute and chronic problems. 
 
According to Figure 2, the single largest contributor to downtime is caused by 
overloading of the AFC drives due to excessive material accumulation on the chain 
conveyor.  The second largest amount of delay time is caused by failures associated with 
the shearer ranging arm. AFC/BSL rank third and fourth in terms of the production delay. 
 
Smart longwall projects 
 
The CRCMining Smart Longwall projects are selected to deliver significant reliability 
improvements on the top 4 delays mentioned above. These are either Acute & Chronic 
problems or Chronic problems in the scatter plot terminology of Figure 3. The focus on 
the remaining top 50 delays is to be maintained through a continuous improvement 
program. The major projects in the CRCMining Smart Longwalls work area are 
described below. 
 
Fault Detection using Citect Data 
 
A Neural Networks model has been developed by Bongers(2003) to detect and identify 
equipment faults using large quantities of data collected on-line through the Citect 
software, which is a remote data acquisition system commonly used in the Australian 
longwall industry. The work so far has proved the feasibility of detecting progressively 
developing failures, including blockage/overload stoppages, temperature trips and 
dupline faults. 
 
The outputs of this project can help the industry improve face equipment reliability in the 
following manner: 
 
Firstly, it improves the accuracy of data recording by automatically generating daily or 
weekly statistics such as: 
 
• uptime 
• downtime 
• no of stoppages in each shift 
• average production delay 
 
Such statistics can be automatically compared against manually entered data for purposes 
of improving record keeping practices. Reliable data recording is essential in selecting 
and implementing projects to deliver substantial improvements in reliability and 
measuring their effectiveness. 
 
Secondly, the fault detection software in its present state can detect the development of 
longwall equipment faults several minutes in advance of the occurrence of each failure.  
The following fault types have been identified with misclassification rates lower than 
20%.   
 
• Maingate Drive Assembly Cooling Fault 
• Armoured Face Conveyor Blockage (maingate) 
• BSL Drive Stall 
• BSL Dupline Fault 
 
Through a continuing program combined with reliable data recording, this list can be 
augmented to cover most if not all of the progressive faults, ie production delays that 
develop over time.  Provided with an early warning about an impending fault, operating 
conditions can be manually or automatically adjusted to avert it.   
 
Shearer Modelling / Pick Force Estimation 
 
CRCMining Cutting Head Performance Analysis Software (CPAS) is able to check a 
drum design against excessive vibrations and inefficient cutting conditions by predicting 
the reaction forces and shaft torque acting on a drum and drawing its breakout pattern 
(Tiryaki, 2003). The software allows engineers to predict the vibration characteristics of 
the shearer drum before it is mounted on the shearer.  This makes it possible to optimise 
the shearer drum design for a particular site, minimising the likelihood of premature 
shearer failures caused by excessive vibrations and force fluctuations during shearer 
operation. 
 
The predictive capability of the software will be calibrated against force and vibration 
measurements from two Australian longwall sites, as part of an ACARP-funded project.  
The CPAS computer model is also used as a virtual machine in a project where 
CRCMining PhD student Anthony Reid is working towards a methodology for on-line 
estimation of individual pick forces.  
 
 
AFC dynamic modelling and chain tension monitoring 
 
The integrity of AFC chain is a critical factor in overall AFC system reliability. The AFC 
chain dynamics play a significant role in this context. 
 
CRC Mining developed a dynamic model of an AFC system including the electrical 
drives and power transmission and implemented the model in a computer program 
(Wauge, 2003).  This program has subsequently been applied to a number of sites to 
study the performance of different AFC drives under different panel geometries and 
loading scenarios. 
 
To verify the model predictions, the CRCMining Smart Longwalls research team 
embarked on a project for in-situ determination of the AFC chain tension in an ACARP-
funded project.  An AFC Chain Tensionmeter was designed and built in the form of an 
instrumented flight bar.  The aim in this project is to measure the AFC chain tension 
under different operating conditions including straight runs, snaking, carrying coal and/or 
stone, stalls and recoveries.  The results of these tests in combination with the AFC 
model predictions will help CRCMining researchers better understand the mechanisms of 
AFC failures and develop operational and maintenance strategies to eliminate them.  
 
 
Investigation of Optimum Maintenance and Production Practices 
 
This is a collaborative project with an actively participating Capcoal longwall site. A 
simplified version of the fault detection and isolation software has been implemented at 
the target site.  This helps the CRCMining research team automatically record the daily 
or weekly statistics of uptime, downtime, number of stoppages and average production 
delays.  Improvement work can be undertaken once failure patterns are identified 
correctly through this work.  
 
Detail review of the previous maintenance planing and operation procedure will also be 
conducted to form the optimum maintenance strategies for the longwall sites as part of 
this project addressing the top 50 delays identified in earlier work.  
 
 
Smart Longwalls Project Findings 
 
This section includes the results of the reliability analysis of longwall equipment and the 
results of AFC simulation. The results of the fault detection are presented in a separate 
paper presented at the CRCMining 2004 Conference. 
 
Findings of the Reliability Analysis 
 
The failure probability distribution functions for the top delays have been identified. The 
identification of the failure probability distributions has significant impact on the 
maintenance strategy since it will reveal the effectiveness of the maintenance activities. A 
failure distribution that has a constant failure rate is called an exponential probability 
distribution and the reliability of the equipment having such category of failure 
distribution cannot be improved by the preventive maintenance. A time-dependent failure 
distribution such as Weibull distribution can determine whether some preventive 
maintenance is effective or harmful since a Weibull distribution may be used to model 
both increasing and decreasing failure rate. If the equipment shows an increasing failure 
rate, the PM may be useful to improve the reliability. If the equipment shows a 
decreasing failure rate, the PM is harmful from the reliability improvement point of view. 
 
All delay data sets are plotted on semi-logarithmic coordinates to test the goodness of the 
exponential fit and log-log coordinates to test it against a Weibull fit. The straight lines 
correspond to the best fitting lines in either case.   
 
The probability plots for number one delay (AFC Blockage) data collected in 2002 in the 
project test site is shown in Figure 4. A Weibull distribution seems to offer a good fit.   
 
 
(a) Exponential fit (b) Weibull fit 
 
Figure 4  Probability plots for Blockage/Overload data  
 
 
The two charts in Figure 5 show the probability plots for data of shearer ranging arm 
failures collected during the years 2000 to 2002 in the project test site. It can be seen 
from this figure that delays caused by the shearer ranging arm do not fit an exponential 
distribution.  The best-fit Weibull curve provides a fair representation.  
 
 
(a) Exponential fit (b) Weibull fit 
 
Figure 5  Probability plots for the failure data of shearer ranging arm  
 
Figure 6 shows the probability plots of AFC chain failure data collected from 2000 to 
2002 fit to both exponential and Weibull distributions. The failure data of AFC chain has 
a better fit to the Weibull distribution.  
 
 
(a) Exponential fit (b) Weibull fit 
 
Figure 6  Probability plots for the failure data of AFC chain  
 
 
Figure 7 shows the probability plots for failure data of the BSL chain collected from 2000 
to 2002 to fit to exponential and Weibull distributions. The Weibull model offers a 
reasonable fit. 
 
 
(a) Exponential fit (b) Weibull fit 
 
Figure 7  Probability plots for the failure data  of BSL chain  
 
 
All top 4 delays seem to fit Weibul distributions but with decreasing failure rate.  This 
means the failure is less likely to occur as time progresses.  Due to the decreasing failure 
rate of the Weibull distribution, it can be argued that preventative maintenance based on 
fixed replacement intervals would not help avoid these faults.  With failure probability 
distributions following these or similar patterns, rigorous PM programs are not 
appropriate.   
 
Findings of the AFC simulation 
 
Two different AFC lengths were simulated and some results are presented in this section.  
 
Figure 8 shows the motor power required to drive a 250m and 400m AFC. The power 
requirement for the 400m AFC is about 1.7 times that for the 250m AFC. The fluctuation 
of power is also significantly higher for the 400m AFC at the same chain pretension.  
 
Fig. 8  Total motor power – 250m and 400m 
 
The fluctuation of the sprocket torque for the 250m and 400m AFC is shown in Figure 9. 
The 400m AFC shows not only higher fluctuation torque but also richer frequency 
content  more peaks on the frequency-domain plot as shown in Figure 10. 
 
 
Fig. 9  AFC sprocket torque 
 
 
Fig. 10 Frequency analysis of main gate sprocket torque for 250m and 400m 
 
Proper chain pretension is needed to prolong chain life and to minimize the risk of chain 
blockage due to slack. Figure 11 shows the power consumption with and without chain 
pretension. It can be seen that the motor power without pretension fluctuates more than 
the motor power with 30t pretension. The same conclusion can be drawn from the plot of 
sprocket torque as shown in Figure 12.  
 
 
(a) No pretension   (b) Pretension of 30 t 
Fig. 11 Main and tail gate motor power 
 
Fig.12  Main gate sprocket torque – zoomed 
 
Figure 13 compares the chain speed at the main gate sprocket to study the pretension 
effect. When there is no chain pretension, the speed of the chain is close to zero after its 
disengagement from the sprocket. This creates significant slack near the main gate 
sprocket. The slack chain near the main gate is dangerous since it could interact with the 
BSL chain and cause the chain to break. A pretension of 30t is predicted to remove the 
slack as shown in Fig 13b. 
 
(a) No pretension   (b) Pretension of 30 t 
 
Fig 13. Velocity of the chain cross the sprocket 
 
Figures 14 shows the variation of chain tension along the face. It can be seen from the 
tension distribution that the fluctuation of tension is smaller on the top deck due to the 
damping effect of coal loading. The power sharing of the maingate and tailgate motors 
can also be identified from the tension on the bottom deck chain. 
 
 
Fig14  Chain Tension along AFC length 
 
Summary 
 
The overall objective of the Smart Longwalls Project is to achieve a significant increase 
in longwall productivity by improving the reliability of face equipment. 
 
The benefits to the longwall industry are: 
• A significant increase in longwall productivity 
• Increased safety 
• Longer useful life of expensive items 
• Improved operating performance 
• A comprehensive database 
• A list of areas where one-off changes must be made to design 
• Optimum PM activities 
 
 
References 
Bongers, D, “Development of a Classification System for Fault Detection in Longwall 
Systems”, PhD Thesis, The University of Queensland, submitted in March 2004. 
 
Knights, P F, “Rethinking Pareto analysis: maintenance applications of logarithmic 
scatterplots”, Journal of  Quality in Maintenance Engineering, 7 (4), 252-263 (2001). 
 
Tiryaki, B, “Monitoring Vibrations”, World Coal, March 2003. 
 
Wauge, D H, “Modelling of an Armoured Face Conveyor”, MPhil Thesis, The University 
of Queensland, 2002. 
 


Reliability improvement through smart longwalls project

http://espace.library.uq.edu.au/view/UQ:100037/Guan_Paper_2.pdf

Reliability improvement through smart longwalls project

Abstract

Similar works

Full text

Available Versions

University of Queensland eSpace