A second systemic look at preventive maintenance

Preventive maintenance is still the type of maintenance of choice for mining and metallurgical organizations today. It is well known, it is a proactive strategy for managing industrial assets that aims to prevent downtime. It is based on a logical sequence of maintenance tasks planned and programmed according to the use of the equipment, the hour of operation or the calendar. Tasks may include inspection, cleaning, lubricating, adjusting, adjusting, replacing parts, testing, and other routine activities designed to keep equipment in good working order and extend its useful life. This method, normally performed on critical production assets, is considered to be profitable, safer and more efficient if the program is properly followed.

However, the discourse of researchers, experts and technology companies on the various platforms swears by predictive and even prescriptive maintenance based on the real condition of the assets. The claim of this discourse is essentially based on better availability of equipment for better annual performance. Preventive maintenance methods based on the real condition of the asset, powered by adapted instrumentation, could remove 10 to 20% of the frequency of maintenance activities from the agenda. In addition, preventive maintenance Cost[certainly cheaper in terms of manpower and equipment.

A legitimate concern is emerging about the risk of carrying out unnecessary interventions. Maintenance predictive , although requiring a greater initial investment in surveillance technology, can often contribute to lower costs in the long run by avoiding unnecessary interventions and by allowing more efficient use of maintenance resources. This effectiveness depends on several factors, including the type of equipment, the complexity of operations, the resources available, and how each approach is implemented.

For the moment, this argument does not seem to arouse people's minds. Businesses generally choose maintenance preventive due to the simplicity of its implementation and management by considering the specific characteristics of equipment and operations to determine the most appropriate approach. Thus, all organizations want to respect their production plan at all costs. Most therefore prefer to stop more frequently for scheduled maintenance and prevent unexpected downtime that can cost hundreds of thousands of dollars per hour in sales. These frequent stops are calculated in the returns qualified as satisfactory. This is therefore considered to be relative security and better control.

Nevertheless, there is a broad consensus among superintendents and maintenance managers that a wider adoption of maintenance methods based on Condition and on risk is inevitable in order to systematize data analysis and have the necessary agility to adapt the maintenance program according to real needs. The adoption of technology-assisted techniques and methodologies can only increase to improve productivity and address major challenges in the industry.

Moreover, many managers have dreamed of artificial intelligence (AI) since the boom we are experiencing. To do this, several actions will have to be taken to centralize, structure and categorize data to give this dream a chance to come true. It should be considered that there is often a significant capital investment required to enable the use of AI. At maintenance department. Equipment is not always equipped with the sensors necessary to obtain a mass of data to change the maintenance mode.

However, are we in danger of making useless interventions? Predictive maintenance requires a greater initial investment in monitoring technology, but it can often contribute to reducing long-term costs by avoiding unnecessary interventions and by allowing more efficient use of maintenance resources. This depends on several factors: the type of equipment, the complexity of the operations, the resources available, and how each approach is implemented.

In this text, we explore three lines of thought in favor of the adoption of technology-assisted methods:

• A second look at maintenance;

• Digitized inspection: helps prioritize actions;

• Creation of collective intelligence.

Maturity in maintenance  

There is still a great disparity between mature organizations and those in search of maturity if we compare them strictly from a maintenance perspective for mining and metallurgical organizations.

For a mature organization, the maintenance program is well established and followed to the letter. Maintenance managers know their equipment well, emergencies are less frequent and production has better predictability on annual tonnage. For the most part, operations and maintenance teams work together, making production much easier.

In a mining organization in search of maturity, the maintenance program is not followed to the letter as much. Maintenance relating to the number of operating hours of certain assets is being stretched or put aside due to emergencies elsewhere on the site. Even if great efforts are made to regain relative stability, maintenance teams act like firefighters extinguishing fires where they start. It's hard to get back on track in this kind of situation. In addition, the risk of catastrophic breakages and accidents may be higher.

Obviously, the motivations for integrating modern computer-aided practices with mathematical models are not the same. Let's look at the nuances.

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A second look at maintenance

There is no doubt that the vast majority of industrial maintenance personnel are highly competent. He must design and follow a solid, effective and comprehensive maintenance program in a context where every minute of downtime is extremely expensive.

Since humans are men, they may miss certain adjustments to the program, which can be crucial or catastrophic depending on the angle chosen. Humans cannot analyze anomalies in real time or detect each of the deterioration trends in an asset park. There is too much data to track, digest, and process, especially since this data is scattered across multiple software programs or systems. So can we really blame them? No, obviously.

In this context, digitized analysis methods based on best engineering practices in integrity and reliability to determine the health condition of a fixed, rotating or rolling asset are necessary additions. APM+ tools make it possible to detect situations, anomalies or micro-events that we would not have otherwise perceived in preventive maintenance. Above all, these detections are faster, which offers more time to adjust the maintenance program and reduce abnormal wear.

To a lesser extent, the detection of anomalies or signs of aging may not generate additional actions to the preventive maintenance program. However, during regular maintenance activities, staff may pay particular attention to additional or simply different items. Thus, equipment instrumentation and comprehensive asset documentation offer vital insurance to the maintenance team. By taking a different and systemic look at the condition and health of the asset, it is possible to cover the blind spots of so-called classical maintenance. This allows information from all the different data sources to be grouped together.

For organizations seeking maturity, technological assistance is all the more necessary. Analyzing trends or events reported by sensor data or inspections prevents unexecuted maintenance from becoming bigger fires to put out.

True stories that systems have made it possible to grasp situations that could have created major problems are numerous. Moreover, in some organizations of different maturity where projects have been set in motion, anomaly detection has proved critical. Serious integrity issues were discovered even though the program did not specifically address certain aspects. This was the case for critical rotating assets such as a mill, but also for fixed assets such as highly damaging sulfuric acid pipes. The incidents avoided saved workers and considerable losses for organizations.

The second digital perspective allows the improvement of the maintenance program. The results of mathematical analyses can certainly become a crucial source of information for daily and operational decision-making. All of this results in tangible gains. Although sometimes presumptuous to calculate for an external firm, the return on investment of accidents that did not occur and the insurance of annual production do exist.

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Digitized inspection: help prioritizing actions

Asset instrumentation is certainly an important element in tracking asset vital signs, but in order to be able to guide the maintenance program and effectively manage assets, inspections are of paramount importance.

What is confirmed is that the digitization of inspections is not yet a given for all mining sites, even for mature maintenance organizations. However, it would be a door to the digitization of operations that is fairly easy to cross. Those who have completed it have mostly done so with form tools that are not adapted to the complexity of engineering in the integrity and reliability of assets.

A tool for creating generic forms, even if the results are transferable to a CMMS, a PowerBI or an ERP, is very inadequate. This is easily explained. On the one hand, the pressure on employees to create complete and effective forms is significant and time-consuming. On the other hand, there will always be a significant lack of functionalities that are vital for tracking data over time.

It is necessary to set up forms dedicated to each type of asset, which are created from an analysis of failure modes, their effects and their criticality, including a severity grid to qualify defects as well as an assessment of the risk associated with each asset. It is important that these forms be supported by an inspection plan structured by an engineer that outlines the work of inspectors. This plan guides the work of internal resources, but also subcontractors, which makes it possible to obtain comparable data during computer-assisted or non-computer-aided evaluations.

The selected product must also be able to read, display and analyze data of several types such as photos, quantitative and qualitative data in addition to thickness measurements (NDT). But above all, this kind of tool should not be an empty box in which all the filling efforts must be made by the organization. Health index forms and calculations should be included. The latter must be based on engineering expertise in assessing the integrity and reliability of assets. She must demonstrate a certain intelligence in order to reduce the work of existing resources rather than continuing the work of trial and error.

Finally, a tool of this kind must offer data traceability allowing the comparison of results with past data and the observation of trends. These results should be displayed on a dynamic dashboard for better visibility on the health of assets and the risks they constitute in order to lead to a better prioritization of interventions. Even mature organizations can expect better performance. Better predictability leads to better cost control. By focusing on the right priorities, we reduce the risk of unexpected emergencies and avoid unnecessary investments in lower priorities.

For organizations in search of maturity, emergencies unfortunately dictate daily tasks. The focus on static assets is virtually non-existent. However, factories are aging and static assets like tanks, pipes, chimneys, and buildings are nearing the end of their lives. These assets are frequently inspected and analyzed by external firms that must inform the organization about the health status of the assets and the risk they involve. These expert reports are often delayed because of the attention paid elsewhere on the one hand, but also because of the difficulty in cutting information and in prioritizing the interventions to be implemented. This lack of visibility inevitably creates latent emergencies that will occur sooner or later and that will jeopardize production.

However, these organizations are the least involved in the implementation of assistance tools. The reason is simple, even if they wanted this kind of tool, they don't have time. They know that implementation cannot be done magically without the involvement of internal resources. The thinking is good, but several APM+ integration teams like that of Stelar bring together teams of engineers, reliabilists, maintenance technicians and IT professionals who would allow them to obtain gains without the perceived mountain of effort. Especially since they can benefit from the support of an engineering firm with the required knowledge in order to set up the models.

Creation of collective intelligence

Over the past few months (2023), we have witnessed improbable situations among organizations that are yet mature in maintenance. To be able to properly analyze the integrity of critical assets in an organization, external integrity and reliability experts have had to call retired people to ask for information on construction plans, materials used, installation dates, Tmins, etc. The most disconcerting thing is that these situations are not isolated.

Metallurgical factories were built in the 50s and 60s. Many critical systems are naturally at the end of their life. We covered it in the article “The relationship between GMAO and Stelar”: investments for the replacement or reconstruction of certain critical and major assets are imposing and sometimes illogical from an economic and ecological point of view.

But how can we expect to extend the life of assets if we don't have the basic information about them? How can we expect to extend the life of assets, if we don't have records of studies, inspections, breakdowns, repairs, extensions, or the like, throughout their life cycle? Asking the question is answering it and it creates headaches.

Over the past few decades, organizations could rely on the knowledge of their best employees. They remained employed by the same organization for years. They knew the site by heart and the assets no longer held any secrets for them, especially since the equipment was still at the top of the life cycle curve. This briefly explains why there was little effect on the sense of urgency to digitize information on the lives of assets. The situation is catching up with organizations.

The challenge is increased tenfold by the situation of the workforce. This is well known, the available and quality workforce is becoming scarce; it stays much shorter; it has to combine several tasks simultaneously because of the shortage; it is retiring en masse. The loss of information is vast and the associated training costs are enormous, trial and error through ignorance, prolonged downtime due to inexperience. Added to this are the growing risks of damage to the health, environment and production of the organization.

It would be unjustified to sound the alarm, but we must recognize and be aware of the risk. Organizations need to re-establish control over their data in order to regain possession of the intelligence of certain key people. They must be in a position to gather relevant new information on the life of assets in a centralized and structured manner. This information is essential to monitor operations as stably as possible so as not to be dependent on the talent or knowledge of a handful of people. We can call it organizational intelligence.

Moreover, young people expect new methods. They have been used to technology making up for certain parts of their lives; their knowledge in the first place. If they have previously worked for modern businesses, they have had access to tools that help them with their work. It is certain that not supporting young people with relevant tools is an obstacle to their integration in a world where arrests are avoidable at all costs.

In addition, mining organizations benefit from organizational intelligence to accelerate the faster implementation of history-based mining projects; faster learning for young people; a better transition between rotating teams; facilitates the work of corporate departments; facilitates the work of corporate departments; facilitates the sharing of information between divisions of an organization for data analysis.

Next step

The transition to predictive maintenance focused on the condition of assets, with the accumulation of centralized and structured information in an asset “medical record”, was an important step in supporting the business goals of companies.

However, although the preventive and prescriptive maintenance strategy has mostly been successful for production equipment such as rotating equipment, predicting which assets are most at risk remains a challenge. These assets are not always limited to rotating equipment, but may also include fixed assets such as a pressure vessel or a pressure vessel.

Therefore, the next step is to implement a maintenance strategy based on risk to identify the most critical assets to be treated. This approach is based not only on operational data, but also on its real state and the consequences associated with a failure.

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