Two up-and-coming uses for technology and big data analytics in the waste and recycling industry work hand-in-hand to ensure fleets and other equipment are working properly based on insight from preceding data.
Predictive maintenance is using previous data to estimate the failure of a part or device. Preventive maintenance is the general upkeep of the device or equipment to allow for near optimal performance and to reduce other failures that can be exacerbated by not maintaining critical parts.
“Predictive maintenance refers to techniques used to determine the condition of trucks and equipment to predict when maintenance should be performed,” says David Biderman, executive director and CEO of the Solid Waste Association of North America (SWANA). “It differs from preventive maintenance because it relies on the actual condition of the equipment, not the average or expected life data, to determine when maintenance is required.”
The fleet maintenance entities of the waste and recycling industry use both preventive and predictive maintenance technology for collection vehicles and landfill equipment. Preventive maintenance also is common in processing facilities to maintain the equipment, including screens and conveyors that have multiple moving parts.
“A large processing system will have multiple full-time mechanics working on preventive maintenance during system downtime,” says Bradley Kelley, senior project engineer for Gershman, Brickner, & Bratton, Inc. (GBB). “Predictive maintenance is less common in the processing sector, as there is less data to utilize for predictions. Most system owners use recommendations from the equipment manufacturer for both preventive and predictive maintenance schedules, although predictive maintenance recommendations are frequently described by ‘as needed’ from these manufacturers.”
The McLean, Va.-based GBB is an international solid waste management consulting firm that helps public and private sector organizations craft practical, customized and technically sound solutions for complex solid waste management challenges. It identifies technology for process facility owners that could be utilized to improve efficiency in preventive maintenance work and to be able to record more data from the machines to be utilized in identifying predictive maintenance items.
“One of the issues with preventive maintenance is making sure it is actually completed and in a timely manner,” says Kelley. “There is software available that will print out daily or weekly maintenance task orders to make sure all items are addressed, as well as record when the tasks are completed to supply maintenance update reports to managers or owners. Unfortunately, most of these software products are oriented toward fleet maintenance and not to system equipment operations.”
Another technology is radio frequency identification (RFID) tags or other such identification and tracking devices. These tags can be placed at strategic locations on a fleet vehicle or throughout a plant to record when a service technician is at that spot to perform the necessary inspections and maintenance. These devices can also be used in garbage and recycling bins to track their use and locations.
“These are helpful in making sure that preventive maintenance is being performed but can also begin to provide data and patterns that can be utilized for predictive maintenance,” says Kelley.
The National Waste & Recycling Association (NWRA) predicts a rapidly changing environment for roadway safety in the coming years, especially with the expanded on-road testing of automated vehicles. Technologies such as automatic emergency braking and vehicle-to-vehicle communication are trying to make the industry safer, according to Kirk M. Sander, vice president of safety and standards for NWRA.
“NWRA recognizes that the safest place for its members’ employees is in their truck cabs and not out on the streets,” he says. “As automation is more deeply integrated into our daily lives, we believe that technologies can be employed to improve the safety of the men and women in the waste and recycling industry.”
Downtime can significantly affect any processing facility. Predictive maintenance technology is one of the best ways to help eliminate catastrophic equipment failures, says Kelley.
“Preventive maintenance is also very important but is time consuming and requires significant human effort,” he says. “Using IoT [Internet of Things] data to streamline maintenance duties would make the overall process more efficient. Not rushing to order parts, having a proper inventory without being overstocked and doing the required maintenance without doing more than necessary would all be financially beneficial to any processor.”
Biderman says it is difficult to predict when a specific garbage truck needs maintenance given the wide variety of operational conditions. The predicted maintenance intervals for a truck likely will differ by geography, size of route, road conditions and time of year. The maintenance cycle for landfill equipment will similarly differ based on use, geography and operational factors.
“It takes data and data analysis to apply predictive analysis,” he says. “When predictive analysis differs from a manufacturer’s suggested maintenance intervals, there could be cause for concern … Companies and sanitation departments should be careful not to implement predictive maintenance primarily as a cost-cutting measure.”
According to Kelley, maintenance crews would adapt to the needs of the facility and equipment for preventive maintenance in the past. There were limited ways available to record these changes and for data to be utilized for studying patterns for improved maintenance efficiency and to start looking for predictive maintenance possibilities. With new sensors and recording technology using IoT emerging, there is data to be able to look for these patterns.
“As the data from IoT devices becomes more and more integrated into software that can more easily reveal patterns, predictive maintenance will begin to overtake preventive maintenance in the processing industry,” he says. “This will make taking an inventory and ordering parts easier and more predictable, and the improved efficiencies will be financially favorable in the long run. This would also greatly improve system uptime by limiting equipment failures by predicting the failure ahead of time and changing the necessary components during normal scheduled downtime.”