What is predictive maintenance and how can it benefit a sawmill?
October 31, 2019 By Bryan Christiansen
As downtime continues to be a major source of concern for sawmills across Canada, it has become necessary to investigate better management of this problem. These incidents of downtime can result in sawmill curtailments or closures with adverse effects on the companies, their employees, and many other stakeholders.
But, what if we were to introduce you to cutting-edge technologies that can help sawmills significantly reduce their maintenance costs when they are operational and thereby cushion the effects of downtime?
One of those technologies is predictive maintenance (PdM). PdM is a technique that allows equipment users to monitor the in-service condition of an asset with a view to predicting any future machine failure before it happens. This is a proactive strategy that prompts maintenance intervention beforehand rather than the more traditional systems of time-based or calendar-based routine preventive maintenance.
Here’s a brief look at the fundamental elements that make up a PdM setup:
1) Condition-based monitoring: Critical assets are identified and fitted with sensors that gather data about the running condition of each equipment. The sensors monitor parameters like vibration, temperature, noise, pressure, electrical current, oil/wear particles, and more. Further analysis of the data generated allows real-time evaluation of the asset’s efficiency and wear.
Note that it is unnecessary to include all machines in PdM. Instead, the focus is on assets that:
- are critical to the production process
- will cause downtime if they reach failure
- are very expensive to repair or replace
The most common types of equipment that are placed on condition-monitoring include rotating equipment, heat generating machines, gearboxes, and electrical components.
2) The Internet of Things (IoT): As the sensors on each equipment gather data, IoT technology allows these assets to exchange information with other connected assets and present the information in a manner that designated technicians can understand.
This information exchange is a major factor that sets predictive maintenance apart from other maintenance strategies.
3) Predictive formulas: Sensor data is collected and compared with pre-established rules about ideal machine behaviour to identify any deviations in the machine’s operation. Predictive algorithms can then estimate when failure will likely occur.
4) CMMS integration: While using predictive maintenance will boost an organization’s maintenance drive, combining it with a computerized maintenance management system (CMMS) delivers a more powerful and centralized system for overall maintenance management.
Among other things, a CMMS will help automate key activities like:
- generating PdM alerts, notifications, and work orders;
- generating records and reports;
- staff training for faster implementation of PdM practices;
- resource monitoring and allocation;
- safety audits and monitoring; and
- planning and scheduling predictive maintenance work.
PdM deployment cuts across almost every industry these days. For sawmills specifically, owners face unique challenges that vary from issues like managing expensive and complex equipment to ensuring operators’ safety. With this in mind, sawmill owners can expect to gain the following benefits from adopting predictive maintenance:
1) Reduce downtime: Predictive maintenance technology protects against unexpected and costly machine breakdown by design, rather than by chance. It’s a simple and straightforward process and, for better illustration, let’s look at an example of how we can monitor wood processing vehicles.
A typical sawmill has loaders, shredders, and material handlers in its fleet and each of these vehicles is assigned to a particular operational site with set deliverables. As expected, if any of these assets breaks down, it could delay production.
But, by installing sensors to monitor heat, noise, vibration, etc., in the transmission, hydraulic pumps, engine, turbo chargers, and rotating parts, operators can receive information about any component that is exceeding its defined threshold. Technicians can then be alerted to perform the required maintenance.
2) Reduce maintenance costs: Back in the ’40s, a British scientist noticed an anomaly: machines that were working perfectly would begin to breakdown more often after routine inspection and servicing. His findings showed that opening up equipment or “interfering” with it for routine servicing increased the chances of a malfunction.
Added to the cost of these service-related breakdowns is the unnecessary waste of replaced spare parts, time, and other associated resources. This has been an on-going problem, but predictive maintenance is changing that narrative.
Instead of tinkering with otherwise functional equipment, sawmills can proactively set up a PdM system and reduce the need for human intervention in the first place. Another long-term benefit is that timely repairs eventually increase the service life of parts through a notable reduction in frequency of repairs and severity of machine damage. It also prevents the multiplication of defects and improves machinery condition overall.
A well planned and executed predictive maintenance project should improve a sawmill’s bottom line by reducing long-term maintenance costs and avoiding costly unplanned maintenance. The benefits are more obvious where the assets in question have to be shut down for significant periods of time before repairs can happen.
3) Better maintenance planning: For busy sawmill maintenance managers, what could be better than systematically taking the guesswork out of planning and scheduling work activities for technicians? With PdM, managers simply assign work well ahead of potential problems based on the information generated from asset condition data.
Predictive maintenance, ideally combined with CMMS, will improve maintenance planning in the following ways:
- Efficient workforce management: PdM helps address the problem of lost work time and underutilized staff. Especially for large sawmills operating in different locations, automating workflow and assigning staff based on required repairs rather than excessive repairs or emergencies will free them to focus on other essential projects.
- Better inventory management: The ability to pre-determine defective parts needed long before a machine fails leads to better inventory management and a significant reduction in required spare parts stock levels.
- Manage repair time: Because predictive maintenance makes it easier to narrow down when a failure could occur, it can reduce the actual time needed for repairs or servicing. Repairs can also be scheduled to a more convenient or cost-effective period.
4) Increased plant safety: There are several safety concerns for sawmills, but two in particular are critical: fire hazards and machine operator safety.
Due to the nature of their operations, sawmills are vulnerable to fire outbreaks. Wood dust and chippings are present while heated machines and engines are working at full speed. Several incidents in Canadian sawmills over the years show that this is a considerable risk.
Monitoring and maintaining fire safety equipment manually can hide unseen loopholes, especially with sites spanning across multiple locations. Fortunately, there are specific PdM sensors designed for fire prevention. These include flame detectors, fire and smoke sensors, and even cloud-based fire detection systems that are remotely monitored. These devices can be installed to monitor areas of the sawmill that are most prone to fire.
It may not yet be possible to completely eliminate human error, fatigue, carelessness, and other factors that cause accidents, but advance warnings of machine and system failures help reduce the risk of machine malfunctions and, by implication, the frequency of personal injury or fatality. In Europe, more insurance companies are now better inclined to support plants that have a condition-based PdM program to mitigate fire losses.
Downtime is now an unpleasant reality for Canadian sawmills. Condition-based predictive maintenance helps to offset the inevitable losses for every time a sawmill is down. From all indications, this is an effective approach for maintenance management in this industry.
Bryan Christiansen is the founder and CEO of Limble CMMS, www.limblecmms.com. Limble is a modern, easy to use mobile CMMS software that takes the stress and chaos out of maintenance by helping managers organize, automate, and streamline their maintenance operations.
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