Machinery and equipment of all kinds, from car parts to cooling systems, wear out over time and must be repaired or replaced. However, in the industrial sector, even a short period of unscheduled downtime can cost millions of dollars. Therefore, companies cannot simply wait for equipment to malfunction and then repair it as quickly as possible; they must devise approaches that ensure their equipment remains operational whenever it is meant to be running.

One popular strategy for achieving this goal is preventative maintenance. Under this system, equipment is serviced on a regular schedule so that necessary adjustments and repairs can be made before there are any external signs of trouble. This schedule may be time-based, but it could also rely on other metrics, such as the number of miles a vehicle has driven or the number of production cycles a machine has completed. While this strategy can significantly reduce unscheduled shutdowns, it still presents several problems. In some cases, time and resources may be wasted on servicing equipment long before this work is needed. Alternatively, unexpected factors could cause equipment to break down before its scheduled maintenance period, resulting in the kind of financial loss that this system is intended to avoid. For these reasons, preventative maintenance cannot offer the same range of benefits as predictive maintenance.

While a similar system in some ways, predictive maintenance incorporates machine learning in order to eliminate the inefficiencies and potential for expensive oversights that characterize preventative maintenance. In order to implement a predictive maintenance system, it is necessary to collect data about the past behaviour of the equipment in question. This data may include temperature trends, readings from vibration sensors, patterns of electricity usage, cycle counts, and other records of important variables.  A predictive analytics engine processes this data in order to discover patterns and identify the warning signs that indicate when a problem is likely to develop. At the beginning of its training, a predictive analytics engine may only be able to detect anomalies without providing insight into their causes. However, by analyzing the outcomes of similar past incidents and testing hypotheses, the engine will develop the ability to dynamically label the situation and fingerprint the precise nature of the problem.

Another advantage of predictive maintenance relates to tribal knowledge, or information that is known only to a limited group of people. Even in environments that use a preventative maintenance system, experienced technicians may come to recognize signs that a problem is emerging, such as a certain noise from a machine. They can then perform unscheduled maintenance to correct these issues, creating the illusion that the preventative maintenance system is functioning more successfully than it really is. However, when these employees leave the company, the tribal knowledge is lost. The workers who replace them must invest time and effort to re-discover the indicators of upcoming problems and learn how to respond to them. In contrast, a predictive maintenance engine uses machine learning to detect and categorize indicators with more speed and precision than a human worker could, and, once these signs are recorded, they won’t be forgotten. Rather than existing as fleeting tribal knowledge, this important data is preserved within the predictive engine and used to inform maintenance insights.

The effects of implementing predictive maintenance at a company can be immensely beneficial. When the engine detects signs that a piece of equipment is likely to break down or malfunction, it dispatches a detailed report, allowing maintenance to occur at the optimal times and reducing unexpected shutdowns to a minimum. As a result, efficiency goes up and lost profits plummet. If you are interested in employing predictive maintenance to streamline operations at your company, contact Terrene.