Using genetic algorithms to improve the performance of medical devices

Medical devices suffer from malfunctions and sudden breakdowns during operation. Device failures in general have become increasingly common, especially since manufacturers have shifted their pricing strategy from the selling price of the device itself to the cost of spare parts. These sudden failures can be mitigated by using a genetic algorithm to improve device reliability, increase the reliability function of the devices under study, and reduce overall maintenance costs, which in turn contributes to extending the lifespan of the equipment. Given the crucial role of maintenance in extending the productive life of machines, devices, and equipment, increasing their availability, reducing the number of breakdowns, shortening repair times, and increasing the interval between breakdowns—all of which positively impact a company’s ability to perform its tasks smoothly and avoid financial losses—maintenance, and specifically preventative maintenance, are key to improving device reliability. Preventative maintenance plays a significant role in avoiding breakdowns before they occur. Therefore, it is essential to identify ways to improve device reliability. The reliability system was developed using an exponential distribution, assuming that downtime and operating times are distributed according to this distribution. The data related to the four sample devices were analyzed to calculate their reliability during the field experience period, and then a preventive maintenance plan was designed for each device. The exponential distribution reliability of the system was estimated using the genetic algorithm method, as it was the best estimation method.