Challenges in asset data for maintenance in mining

Maintenance of mining equipment is crucial to protect capital investments and support operational uptime, with accurate asset data aiding in maintenance planning.
January 18, 2024 by
Pedro Jr.

Mining operations are complex, often requiring expensive machinery designed specifically for the task. Therefore, maintenance of mining equipment is crucial to protect capital investments and support operational uptime. A critical failure in an asset can cause operational disruptions, resulting in significant losses depending on the repair duration. Losses can quickly amount to hundreds of thousands of dollars or more. With such a significant operational impact, it is understandable that maintenance costs in mining are high, especially when over 60% of the total mine workforce may be almost exclusively focused on servicing or repairing assets in the mine (source: Miningglobal.com).

The maintenance goal for most mining companies is to ensure their equipment is in good working condition to maximize productivity, minimize downtime, and reduce maintenance costs. This means maintenance teams must enhance the reliability and availability of physical assets to maximize employee safety, minimize risks, and reduce operating costs. Data is at the core of helping mining companies achieve this goal, but current challenges in asset data result in:

  1. Business decisions based on unreliable or unavailable data.
  2. Inefficient utilization of maintenance resources and personnel.
  3. Difficulty justifying asset replacement and redesign strategies.

Understanding the contributions related to asset data to each of these challenges provides insights into the capabilities necessary to address them.

Reliability and Accessibility Issues with Data

Reliable and accessible data are essential for informed business decision-making. Unfortunately, many mining companies struggle with one or more of the issues identified as:

Key Problems Contributing to Misinformed Business Decisions:

  1. Older equipment that may not have been designed with data collection in mind, such as limited sensing capability.
  2. Independent equipment not integrated into the infrastructure.

Distributed Asset Data in Disparate Systems:

  1. Asset data directed to different systems (e.g., DCS, MES, CMMS).
  2. Data and systems not integrated on the network or not sharing data across a network.

Asset Data Without Any Contextualization:

  1. The way data is displayed or available limits context.
  2. Data lacks accompanying analysis tools to easily extract meaningful value.

The lack of good asset data can lead to overly cautious maintenance approaches that increase operating costs without necessarily adding value.

Inefficient Utilization of Maintenance Resources

Without access to reliable asset health data, mining maintenance leaders often find themselves either over-maintaining or allowing failure. Over-maintaining equipment to reduce the risk of unplanned failures can cost companies much more in lost production and downtime than excessive maintenance costs. Limited maintenance staff and equipment in mining companies are applied to maintenance tasks that may not be prioritized based on pending failure data. Asset health data can provide valuable insights to help maintenance programs find appropriate preventive maintenance cadences to allocate resources to more imminent maintenance issues.

Difficulty Justifying Asset Replacement and Redesign Strategies

When asset health data is unavailable or not accompanied by analysis tools, it can be challenging to identify the specific source of failures. Even if data is available, if it's in spreadsheets or distributed across multiple systems, drawing accurate conclusions about failures can be difficult. The ability to analyze asset health data across the operation and recognize significant trends can help maintenance organizations improve long-term maintenance strategies, such as prioritizing asset replacements or redesigning specific processes or equipment. Having data available to support decisions like asset replacement streamlines the financial justification process and facilitates obtaining support for maintenance priorities.

Key Elements to Address Challenges in Asset Data

How do mining companies begin to address these data challenges and move toward improvements in maintenance and operational efficiency? One answer is a unified Asset Performance Management (APM) solution. A singular holistic view of equipment and asset health on a site or even across multiple sites can provide mining companies with the insights needed to optimize maintenance.

Some key elements of an APM solution that can help mining companies recognize the most value include:

  1. A specific solution providing asset health functionality tailored for mining, with predefined models. Quick setup with minimal engineering effort is essential.
  2. A hierarchical asset approach to help efficiently track, schedule, and identify failure sources.
  3. A solution that can easily integrate with existing systems, data sources, and applications.
  4. A cloud-based approach, which can minimize infrastructure costs, avoid adding IT workload, reduce setup time, and improve accessibility to asset health data.
  5. Scalability, allowing mining companies to add additional assets, analytics, and other features at their own pace.

Summary

A robust and effective maintenance program is composed of many integrated parts. A single software, system, or tool will not provide instant improvements in maintenance. However, asset health data is at the core of delivering operational improvements. Asset Performance Management solutions can be the enabler by providing a holistic view of the health of mining assets to transform equipment data into contextualized asset intelligence. How a mining company interprets and acts on this asset intelligence to inform its business decisions will determine whether they achieve their desired outcomes, such as increased revenue, higher asset efficiency, and reduced operating costs.


Share this post
Tags
Archive