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Reliability Centered Maintenance (RCM) for Automated Mining Machinery (Mechanical/Computer Project)


Reliability centered maintenance (RCM) was initiated on 1960s in Boeing company to optimize the maintenance process of aircrafts. Since that date, this method has been applied in wide range of industries and has provided a completely positive results and recommendations for implementation in other industries.

RCM is a systematic approach to quantitatively assess and optimize the performance of preventive  maintenance tasks and to eliminate non-value adding maintenance actions. It provides considerable cost savings due to optimum effort, increased safety and productivity.

This research considers the feasibility of applying the RCM methodology to fully automated underground mining machineries as one of the vital requirement of early future modern mining. For this purpose, a literature review has been done to clarify the advantages, requirements, issues and challenges of RCM in other industries such as aviation, marine, nuclear, oil and gas, and process industries.

It has been tried to analyze the RCM procedure in detailed and to have a look on the adoption issues and requirement for RCM implementation in fully-automated mining.  Mainly, in this research, following RCM documents and standards were used for feasibility study:

  • Classic RCM in Aviation industry (SAE-JA1011, SAE-JA1012)
  • NASA RCM guidelines
  • USA’s military standards MIL-STD-2173
  • International Atomic Energy Agency (IAEA) RCM document

Using the above mentioned documents, an implementation issues and challenges in developing a RCM program  for  fully-automated underground mining machineries has been presented. The result of this study shows that RCM is applicable in maintenance  planning for fully-automated underground mining machinery.

Because, serious safety restrictions are associated with this kind of mining operation and RCM can properly help the engineers to analyze the safety consequences of any failure and make the best decision for maintenance tasks. However, practical  application  of RCM has some differences in mining context which in this project are discussed in detail.

The investigations show the risk number is the suitable measure to select the RCM target  component/system. Since, there is no operation in site, detective the so me evident failures are become impossible in automated mining. Therefore, we have to consider the smartness level and capabilities of agent based supervisors to get the real feeling of machinery health and operation  condition.

Internet of Thing platforms are also required in fully automated mine to develop the  machine-to-machine communication and to reduce the risk of failures and failure propagation in fleet level. RCM could apply the outcomes of these advanced technologies to optimize the maintenance actions in automated mines.

Typical IOT and Cloud Computing Platforms for Automated Mining Machinery.

Typical IOT and Cloud Computing Platforms for Automated Mining Machinery.


 Automated Mining Machinery:

With modern changes in technology, mines have become increasingly mechanized and automated. As shown in Figure 1, heavy machinery was introduced to the mining industry in the 1950s. A second major wave of change began in the 1990s with the use of computers. More recently, especially since 2000, concomitant  demands for more raw minerals and higher safety levels have forced mining companies to think about changing their methods of operation, along with their machinery.

Figure 1. Technology Progress in Mining Machinery.

Figure 1. Technology Progress in Mining Machinery.

Structure and Types of Automated Systems for Mines:

The mining operation of the future is likely to be a bit eerie, combining driver less trucks, drills and haulage trains, with plant controllers monitoring operations remotely from central control stations kilometers away. Mine automation covers everything involved when we try to replace human senses and intelligence with machines, including sensor technology, communication network and devices.  The main four subsystems of automation are: control stations, communication systems, safety systems and machinery.

Maintenance Challenges in Full Automated Mining Machinery:

Maintenance plays an important role in an effective mine. Through short daily inspections, cleaning, lubricating, and making minor adjustments, small problems can be detected and corrected before they become a major problem that can stop production. Maintenance should keep systems functioning so a company’s goals can be achieved.

This includes meeting the requirements of CRAMP parameters (Cost, Reliability, Availability,   Maintainability, and Productivity) for any automated systems. A holistic approach works best, one able to integrate the evaluations, not only of the systems themselves, but also of their interactions with each other and their environment.

Systemic Approach to Maintenance (Mining Application).

Systemic Approach to Maintenance (Mining Application).


Definition and History:

With the advent of the widespread use of commercial jetliners in the 1960, the Federal Aviation Association (FAA) became increasingly concerned with safety issues, including programs of Preventive Maintenance (PM)  tied to aircraft type. This led the commercial  aircraft industry to completely re-evaluate its preventive maintenance strategy, including a review of why maintenance was done and how it could best be accomplished. United Airlines led the way, with Bill Mentzer, Tom Matteson, Stan Nowland, and Howard Heap becoming the pioneers of this type of research.

RCM Principle:

RCM methodology has been originated based on four main principles which are known as pillars for RCM philosophy. Smith and Hinchcliffe have listed these principles as following:

  • Preserve system function
  • Identify failure modes that can defeat the functions.
  • Prioritize function need (via failure modes).
  • Select applicable and effective PM tasks for high priority failure modes.
  • System-Focused
  • Reliability-Centered
  • Acknowledges Design Limitations
  • Safety, Security, and Economics
  • Failure as Unsatisfactory Condition
  • Logic Tree to Screen Maintenance Tasks

RCM Procedures:

RCM is a step-by-step process, with seven steps proposed to systematically delineate the  information required to finalize the maintenance programing:

Step 1: System selection and information collection.
Step 2: System boundary definition.
Step 3: System description and functional block diagram.
Step 4: System functions and functional failures; preserve functions.
Step 5: FMEA; identify failure modes that can defeat the functions.
Step 6: Logic (decision) tree analysis; prioritize function need via failure modes.
Step 7: Task selection; select only applicable and effective PM tasks

RCM Applications in Different Industries:

Along with providing safety, security, cost reduction, reliability enhancement, maintenance
scheduling, and efficiency improvement, RCM is applied in different industries to answer the following questions:

  • What does the system or equipment do? What are its functions?
  • What functional failures are likely to occur?
  • What are the likely consequences of these functional failures?
  • What can be done to reduce the probability of the failure, identify the onset of failure, or reduce the consequences of the failure?


The RCM process and reviewed the extensive literature on RCM implementation in industry. In  this  chapter, we consider the possibility of adapting the RCM technique to automated underground mining machinery. We begin  with  a review of RCM  application to mining machinery more generally and then discuss its use on automated mining machinery specifically.

Review of RCM Application in Mining Machinery:

Reliability and maintenance issues have a significant impact on mine operation and its profitability, so much so that many mining companies have identified maintenance and its  management as a strategic area for research and development. Modern production equipment is sophisticated  and  capital  intensive,  so keeping standby units often represents a prohibitive cost.

As a result, most of the mining systems have little or no redundancy. At the same time, operating conditions, particularly underground, are extremely harsh, with numerous equipment failures that are difficult to predict or prevent. With limited redundancy, many such failures may severely affect mine  production and lead to substantial losses.

RCM Adaptation to Automated Mining Machinery:

In this section, we look at RCM implementation in fully automated mining machinery, drawing  on the following documents and standards:

  • Classic RCM in aviation industry.
  • NASA RCM guidelines.
  • USA military standards MIL-STD-2173.
  • International Atomic Energy Agency (IAEA) RCM document.
Main Subsystems of Underground Automated Mines.

Main Subsystems of Underground Automated Mines.


In this project we discussed about the Reliability Centered Maintenance (RCM) as a well-known maintenance planning and optimization method. RCM applies all maintenance strategies and tools to enhance the reliability and availability of analyzed machinery.

The different maintenance strategies in a typical  RCM  program. Considering  the  kind of components and failure modes, RCM can flexibly take the analysis roles and could provide an applicable and effective solution in a systematic way.

This project has studied the feasibility of implementing RCM for automated mining machinery. Literature review on other industries and our investigations show that RCM has good capability to control and maintain the safety and risk associated failures; it could  yield benefits in the maintenance optimization of automated mining machinery and is clearly applicable in this field.

Because  of  the integrated nature of automated mining  (all  machines are  connected  to  a central automation platform in a control room) failure prevention is vital. By controlling failures, RCM could  enhance the operational  availability of the whole production fleet. However, the RCM process must be adapted for automated mining production, considering the special characteristics and harsh operating contexts of mining.

The recommendations for adaption and getting better results from RCM analysis are presented here. However, it should be considered that the best and final adaption advises could be presented only by carrying out real case studies in automated mines.

Therefore, following comments are just the results of comparative investigations between the  basic RCM philosophy, achieved experiences in other industries considering context of automated mining:

  • As the cost of maintenance and safety are two main factors in maintenance effectiveness, in the first step of the RCM process (system  selection) applying RPN as the system selection criterion is  suggested. The average value of RPN is calculated based on the  maintenance cost and safety consequences of failures. According to the literature, two years of failure history data are recommended for failure analysis and reliability modeling to support the RCM process in automated mines.
  • System boundaries are defined for machinery and on-board components.
  • RCM implementation in mining automated machinery in case of PM optimization is limited to mechanical and  electrical components and subsystems subject to degradation and wear out, which can be monitored.
  • When applied to the software of automated mining machinery, the FMEA process has a different concept of failure than in hardware systems; this means it needs to be more carefully analyzed. The failure mode and effects analysis for hardware or software have certain distinguishing characteristics. It is important that to remember in the field of mine automation software, failures usually happen in a “functional” or “interface” perspective.
  • In fully automated mining machinery, there is no operator, so being aware of a failure occurrence can be difficult. Therefore, we have to modify the failure mode categorization criteria, perhaps even change them. The operator can be replaced by supervisory systems such as agent-based  supervision,  or  “Smart  Agents”  playing the role of operator in intelligent operating machinery.
  • The use of the “Internet of Things” platform, including eMaintenance and eOperation, is strongly  recommended  in  combination  with  RCM  in  automated  mining  machinery. This enables us to control failure propagation in the mining fleet.
  • Since a mining operation often has a large inventory, we rarely face a full outage problem. As long as production continues, even at a low rate, any failure is counted as minor, or in a worst case scenario, as a high cost failure.
  • Safety related problems are very important in mining; therefore, eliminating safety problems is the overall  goal  of  automation,  so mething to consider in all maintenance strategies.


In recent years, as instrumentation and information systems become cheaper and more reliable, CBM becomes an important tool for running the production systems. Traditional CBM is a maintenance program  that recommends maintenance actions based on the information collected through condition monitoring.

The previous CBM carries out maintenance task that focuses only on condition monitoring and diagnostics. With increasing requirement in predicting future degradation trend  of  equipment  performance,  CBM functions extended and a prognostic layer was added.

In recent years, a  development of CBM called CBM plus (CBM+) is put forward, which is the application and integration of appropriate process, technologies, and knowledge based capabilities to improve reliability and maintenance effectiveness.  At its core, CBM+ is maintenance performed on evidence of need  provided by RCM analysis and other enabling processes and technologies.

Since automated mining machinery get benefits of high technologies in condition monitoring and operation control, wide range of machine health data is available for making better maintenance decisions.

Therefore, one of the possible and attractive research works which could be  carried  out on automated  (currently  semi-automated) mining machines in future, is to develop an  advanced CBM+ system that uses RCM mechanism to optimize maintenance cost, and employs data fusion strategy for improving condition monitoring, health assessment, and prognostics.

Source: Luleå University of Technology
Authors: Seyed Hadi Hoseinie | Uday Kumar

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