Preventive and Predictive Maintenance with Martin Engineering

Martin Engineering process engineering Daniel Marshall shares how preventive and predictive maintenance can support the health and safety within the quarrying sector. 

Effectively servicing high-tonnage conveyor systems is crucial for sustaining production and profitability in a quarry. Maintenance, repair, and operations (MRO) professionals need a comprehensive plan focused on workplace safety. Developing such a strategy requires understanding the advantages and disadvantages of reactive maintenance (RM), preventive maintenance (PM), and predictive maintenance (PdM). The goals include minimising unscheduled downtime, improving safety, enhancing efficiency, and reducing overall operational costs to achieve a better return on investment (ROI). 

Reactive Maintenance 

Even today, many companies engage in what can be termed reactive maintenance on their conveyors, meaning they merely repair whatever breaks down.  Components continue to operate until they fail, leading to unscheduled system downtime that is both disruptive and costly.  Factors contributing to the expenses of this method include unplanned production stoppages, damage to ancillary equipment (when a broken component causes harm to other parts), and fees for overtime and emergency services. 

Other disadvantages include a shorter asset life expectancy, as components are not maintained in optimal running condition, along with uncontrolled budgets and potential safety hazards.   Additionally, technicians tend to take more risks and make more mistakes when pressured to restore operations in the shortest possible timeframe.    

Despite the clear downsides of a predominantly reactive maintenance strategy, it is estimated that half of all conveyor maintenance activities in the average North American facility adhere to this approach.  The main reason is budget: reactive maintenance requires fewer staff, less planning, and a lower initial investment.  However, such a strategy results in ineffective planning, insufficient oversight, and far less system control. 

The Shift to Preventive Maintenance  

The deficiencies of reactive maintenance have driven an evolution to a more preventive approach to minimise failures that force unplanned shutdowns for repairs.  Guidelines are typically based on time in service or operating hours.  The idea of PM has been around almost since the invention of the wheel: performing regular maintenance on equipment to reduce the chances of failure.  It’s the same approach that consumers use when they take their car in for scheduled oil changes to extend engine life.

PM extends component life, boosts productivity, enhances overall efficiency, and lowers maintenance costs. However, bulk handling equipment experiences continuous stress from transporting millions of tons of rock, aggregate, and sand in varying weather conditions, potentially resulting in dust emissions, spillage, and carryback. An effective PM schedule requires strict adherence and regular updating.

Some firms handle this responsibility in-house, while others discover that specialised service providers yield a better return on their maintenance investment. They depend on the expertise and scheduled maintenance from industry experts, allowing their employees to concentrate on core activities.    

Generally triggered by time, metered inspection, or common indicators (such as mistracking and spillage), the PM approach to conveyor maintenance assumes that each component has a typical equipment life based on previous similar applications and environments. By using observation and experience, PM determines when relevant parts should be retired, replaced, or refurbished before the expected failure point. The result is generally greater safety, higher system efficiency, reduced spillage, and better overall system control. 

Predictive Maintenance Implementation 

A predictive maintenance program begins with data collection and storage, followed by analysis. In the past, adding new measurement points was a time-consuming and expensive task, but wireless instruments have significantly reduced these costs.

With more accessible data collection, storage, and analytics options, some bulk material handlers are realising that each critical component can and should be monitored and analysed to optimise performance. Sensors can also transmit data to cloud-based software, which then relays it to mobile apps in the field.   

The initial capital expenditure for these systems may seem steep, but cloud-based technology can offset some of the cost of entry. The benefits of extended equipment life, tighter budget forecasting, more reliable maintenance scheduling, increased worker efficiency, decreased downtime, and improved productivity all contribute to a swift ROI.  

Machine Learning 

Unlike PM, which relies on wear life determined by the manufacturer and/or operator observations, machine learning adapts maintenance needs to the operational and service environments, fueled by all previous input. The benefit is a tailored experience that creates the impression of equipment directly communicating its needs to decision-makers.  

A recently commercialised example is a belt cleaner position indicator that monitors the blade, tracking and reporting its remaining service life. The intuitive device continuously gathers data on primary belt cleaners, notifying factory-trained service technicians and plant operations personnel when re-tensioning or replacement is required or when abnormal conditions occur. Managers and service technicians can quickly access information on any networked cleaner via cell phone or Wi-Fi.   

The device provides crucial real-time intelligence and minimises worker exposure to moving conveyors, enhancing both efficiency and safety. Maintenance planning becomes easier with detailed information available on demand, enabling service personnel to deliver and install replacement wear parts during scheduled outages. By relying on actual operating conditions rather than human judgment to monitor blade wear and tension for optimal cleaning performance, the indicator maximises the blade’s usable surface area and accurately reports when a blade is nearing the end of its useful life.   

 Taking the technology a step further are sensors that continuously monitor blade pressure and inform stakeholders about their wear status. Maintenance personnel no longer need to visit each cleaner to manually re-tension. This reduces maintenance time while maximising the usable area of every cleaner and elevates the concept of preventive maintenance. Rather than optimizing for a process parameter or other metric, the approach prioritizes real-time profitability as the desired outcome.   

The Cost of Preventive Maintenance 

Equipment life estimates often do not account for performance in varying service conditions, so calculating cost projections based on failure rates drawn from data of similar applications remains largely theoretical. In most situations, PM can be no more than an educated guess, taking into consideration the varying conditions, application, and operating schedule, among several other variables. However, a primary issue with PM is that some parts will inevitably be serviced too frequently, driving up costs, while others will not be serviced often enough, leading to degraded performance at best and catastrophic failures at worst. 

Maintenance of bulk handling systems also depends on the availability of parts, the age of equipment, trained labour to perform the work, and the regulations surrounding each procedure. Operations such as coal-fired boilers, cement production, and smelting plants may require cool-down and ramp-up periods that can take days on either side of the maintenance work and may also introduce additional safety concerns (personal protective equipment, confined space entry, exposure limits, etc.).   

Predictive Maintenance 

Predictive Maintenance (PdM) directly monitors equipment performance during normal operation to anticipate failures more accurately. Relying on sensors and supported by software, it collects information over time, aggregates the data, and uses an algorithm to  

deliver practical results that are made available to stakeholders. When combined with regular physical inspections, this type of data-driven system provides much more complete, accurate, and actionable information for service technicians and operations personnel. 

 Some component manufacturers provide structured conveyor inspections and belt cleaner maintenance as part of a managed service relationship. Their monitoring systems can track component wear and notify the service technician and/or operations team via Wi-Fi or cell phone about upcoming service needs. The technology also sends alerts through a mobile app in the event of upset conditions, enabling service technicians and plant operators to access real-time data. Some new systems can even automatically adjust belt cleaner tension.   

Highly trained service technicians provide an additional set of eyes on the conveyors, travelling to and from the equipment to be serviced and logging details in their reports. Because they encounter so many different applications, they can often identify problems that general maintenance personnel overlook or have become accustomed to ignoring. With factory-direct managed service, the responsibility for maintenance rests with the provider, allowing the staff to focus on other priorities. 

Unlike PM, which is determined by an average or expected life statistic, PdM is based on the actual condition of the equipment. Sometimes called “condition-based maintenance,” when predictive analysis identifies a potential issue, the repair can be scheduled at a time that minimises the impact on production. The benefits include further optimising system performance and component life, reducing the need for visual inspection, and minimising guesswork through a more automated, analytics-based system. Although it doesn’t fully eliminate the need for personal inspections and maintenance, for conveyor systems that can be miles long and, in some cases, cover difficult terrain, this approach saves time and reduces potential hazards. 

Combining the Power of PM and PdM 

Predictive maintenance offers numerous advantages over preventive maintenance, but in the past, it was often too costly or impractical to implement this strategy for all but the most critical components. Now that data collection, storage, and analysis are becoming easier and more affordable, additional components and systems are likely to be incorporated into a plant’s conveyor maintenance program. 

Bulk material handling systems operating in harsh environments experience unexpected failures that can be difficult to predict, especially when caused by random overloads or human error. Therefore, maintenance staff need to be able to react to sudden failures. During unscheduled downtime, managed service providers can take advantage of the outage to maintain or upgrade equipment.  

Some service providers are also taking steps to help customers whose facilities have limited access during the virus pandemic by partnering with their maintenance staff to remotely train employees on effectively maintaining their conveyor systems. They offer guidelines on preventive maintenance, inspections, and replacement blade options. Factory-direct technicians remain in close contact with periodic check-ins and operate within key parameters to ensure optimum performance. 

 With the goal of creating an efficient and cost-effective maintenance schedule, human labour will always be necessary to design and implement solutions, just as data will continually depend on human experience for proper application. That’s why leveraging the benefits of both PM and PdM represents the most effective approach. Given that unscheduled downtime, prematurely worn-out parts, and unnecessary labour significantly impact operational costs, integrating both methods into a maintenance strategy enhances the effectiveness of each. 

 

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