Definition:
An analysis of payload distribution for a fleet of Off Highway Trucks expressed in terms of Caterpillar’s 10/10/20 truck overload policy.
Description:
The application that a piece of equipment is used in has a direct impact on the overall performance of that equipment. Payload management, the extent to which haul trucks are operated within safe and commercially acceptable limits, is an important consideration when assessing application severity. The pressures of ever increasing production demands have driven the mining industry to employ haul trucks and loading tools of increasing size and capacity. Furthermore, mines have recognized that reducing truck-loader pass match, frequently to three to four pass loading, will yield production advantages, at least in the short term, by reducing the time that the haul truck sits under the loading tool.
Unfortunately, the factors that tend to benefit production via reduced per unit haulage costs also tend to reduce a mines ability to manage payloads within recommended limits thus increasing operating costs due to their adverse impact primarily on the engine and power train components, structures, the suspension system, dump body and tires. Other factors such as normal variations in material density and moisture content, material blast fragmentation, bucket size, loader operator skill level and material carry-back/ debris add to variability thus complicating the task of payload management. With this in mind, payload management should be considered a key
performance indicator for assessing application severity for a fleet of equipment and the equipment manager should be diligent in the ongoing evaluation of a mines payload management practices through ongoing documentation, tracking, and trending of payload data.
Calculation Methodology:
Payload management can be quantified using one of the following methods:
• Actual count analysis … data from the Truck Payload Management System
(TPMS) or VIMS-TPMS reports can be used to count the actual number of loads within each of the ranges defined by the 10/10/20 policy and compared for compliance with the guidelines specified in the documentation. VIMS Supervisor also has the capability to evaluate payload management in terms of a histogram; cell size and cut-off limits are important considerations when performing payload management analysis using the histogram.
• Statistical analysis … data from TPMS or VIMS-TPMS reports can also be
used to perform a statistical analysis for loads within each of the ranges defined by the 10/10/20 policy for comparison with the guidelines specified in the documentation. Software such as Excel makes this type of analysis relatively simple. The analysis assumes that the data is represented by a normal distribution (bell-shaped curve).
Before beginning the user should view a frequency distribution of the data to verify that the data being modeled is indeed normally distributed since normal variations and operating practices on a mine can cause the data to follow a bi- modal, skewed or other distribution. If this is the case, the model predicted by the statistical analysis will be invalid.
Once again, the cut-off limit is an important consideration when performing payload management analysis using the statistical approach. Experience has shown that ignoring loads that are less than 50% of the target payload will closely duplicate the actual distribution of data. Eliminating zero and very small loads is valid since it is the upper limits of the distribution that we are interested in defining.
Data Source(s):
The specifications for gross machine weights can be obtained from the Caterpillar Performance Handbook, machine specification sheets, and information contained in the factory documentation for the 10/10/20 overload policy.
Since empty weights published by the factory represent generic approximations, actual empty machine weight is best obtained from scale data. Factors such as body design, tires, optional equipment, and carry-back or other debris accumulation can have a significant affect on the accuracy of published estimates.
Payload data is obtained from the Truck Payload Management System (TPMS) or VIMS-TPMS reports.
Benchmarks:
There is no Benchmark that is applicable to the payload management metric. Target performance should be compliance with the 10/10/20 policy.
There is however a Benchmark that applies to the standard deviation of payloads in a given population. Best performance that we have documented is a standard deviation (which defines and limits the shape of the curve) equal to 6 ½ percent of the target payload. This level of variation among payloads, combined with a reasonably well- matched bucket to body size capacity is most likely to result in optimum payload management performance.
Usage:
Application severity indicators such as payload management enable the equipment manager to gain an understanding of the application of the equipment, to determine when and why the application changes, and to adapt or modify his maintenance strategy, when appropriate, as dictated by changes in the application or operating environment.
Interpretation:
Accurate analysis and interpretation of payload management requires an understanding of the terminology used to quantify and define it. The target payload is the difference between the gross machine operating weight and the empty operating weight. The maximum (never to exceed) gross machine operating weight is 1.2 times the target payload.
Caterpillar’s 10/10/20 truck overload policy states that “The mean (average) of the payload distribution shall not exceed the target payload, no more than 10% of payloads may exceed 1.1 times the target payload, and no single payload shall ever exceed 1.2 times the target payload.” Due to the fact that optimum conditions result in a payload distribution in which the standard deviation of loads is equal to 6 ½ percent of the target payload, this level of performance represents the best possible payload distribution in most applications. Hence, in most cases the mean of the payload distribution will be something less than the target payload in order to achieve compliance with the 10/10/20 policy. The exception occurs with a truck-loader pass match of six or more (which compromises production and is therefore relatively rare) or when trucks are loaded by conveyor.
It should be noted here that because of the distribution required to comply with 10/10/20, 50% of the loads will be less than the target payload and, as such, some mines may view this as chronic underloading. A better approach is to view payload management not only in terms of 10/10/20 but also as the percentage of payloads occurring within a range (+/- 10%) about the target payload. In this case optimum
loading performance within the guidelines of 10/10/20 results in 80% of loads within range, 10% > 1.1 times target payload, 10% < 0.9 times target payload, and no loads > 1.2 or < 0.8 times target payload.
Action:
If payload management practices fall outside the limits of the 10/10/20 policy or are trending in that direction, the equipment manager should investigate as follows:
• Analyze payload management on an individual truck basis to determine if one or more trucks are experiencing problems with the truck payload measurement system. A malfunctioning system will induce errors that can cause the fleet distribution to be skewed or perhaps even bi-modal, which will invalidate the analysis. (Some degree of random variation is to be expected due to the inherent inaccuracies of the system but gross variations are most likely a result of system malfunction or calibration errors).
• Work with Operations to determine the source of the overloading.
Overloading may be the result of variability in material density, the use of loading tools of varying size and bucket capacity, or operator training issues. In addition to TPMS, payload “scoreboards” and other aftermarket systems are available to better define and manage the loading process. If the issues cannot be resolved, the equipment manager should document the problem in writing to the mine in the context of the MARC or other agreement since, in all likelihood customer expectations will not be met.
• Increase the frequency and quality of structural inspections. Overloading will result in earlier than expected frame and structural damage particularly when combined with rough haul roads and high-speed operation. More thorough inspections will facilitate early detection and repair before failure.
• Re-evaluate the component management strategy. Overloading has a
detrimental impact on component lives (and related costs) requiring increased emphasis on condition monitoring and in some cases a revision of the component replacement plan.
• Review the condition monitoring plans for machine elements such as tires, brakes and the steering system. Overloading will result in excessive tire wear and failure due to heat separation, accelerated brake wear, and brake and steering system overheating and failure. This may require that trucks are rotated to less severe hauls or perhaps even pulled out of service temporarily until stabilized and acceptable temperatures can be attained.
Has Impact On:
• Production, ... optimized payload management will yield long-term production advantages that result from better availability and higher speeds on grade. That is, any short-term production benefits that may result from overloading will be
more than offset by the increase in machine downtime and reduction in loaded travel speeds that result from overloading.
• Maintenance costs, … the cost per operating hour of engines, power train components, structures, the suspension system, dump body and tires as well as that of maintenance manpower and repair facility requirements will increase with overloading.
• Operating costs, ... fuel consumption and the associated fuel costs per operating hour will increase with overloading.
• Safety, … overloading can result in a condition in which the machine is operating outside the certification limits of the brake and steering systems.
• Haul road maintenance, … haul road damage as a result of overloading will increase necessitating additional maintenance.
Is Impacted By:
• Payload measurement system operation, ... any malfunction of the payload measurement system, e.g. system calibration, strut charge, strut sensor operation, etc., on one or more machines will result in erroneous load data that will produce false indications of change in for the fleet.
• Mine production requirements, ... an availability shortfall or increase in the production demand may result in intentional overloading in the interest of short-term production gains.
• Operating practices, … backing onto the toe of the cut, load placement in the dump body, and tamping the load with the loader bucket will impact payload measurement system accuracy. (NOTE: The second gear reweigh feature in TPMS addresses this issue for capturing payload data, however this is still an issue when TPMS is used to monitor payload during the loading process).
• Material density, … normal variations in material density as well as those that result from variability in seasonal precipitation, i.e. material moisture content, will complicate payload management control practices.
• Truck-loader pass match, … while it may yield short-term production
advantages, three to four pass loading will make the task of payload management much more difficult.
• Bucket fill factor, … muck pile variation that results from blasting practices and/or material loadability as well as normal variations in loader operator skill levels may create problems for payload management.
• Bucket-dump body capacity, … loader buckets that incorrectly sized to the dump body will result in problems for payload management.
Presentation Format:
Payload management data should be collected, analyzed and reported monthly. Trending monthly payload management data over time for a twelve-month period as illustrated by the graph below is the most visual and effective method to define trends in payload management performance.
Figure 9: Sample OHT Payload Management trend 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15% 16% 17% 18% 19% 20% 21% 22%
May-03 Jun-03 Jul-03 Aug-03 Sep-03 Oct-03 Nov-03 Dec-03 Jan-04 Feb-04 Mar-04 Apr-04
DATE (Mo-Yr) % o f L o a d s > 110% o f T a rg et 0.00% 0.20% 0.40% 0.60% 0.80% 1.00% 1.20% 1.40% 1.60% 1.80% 2.00% 2.20% % o f L o a d s > 120% T a rg et
Management Limit (points below this line meet the "10-10-20" criteria)
Management Limit (points below this line meet the "10-10-20" criteria)