AI Use Case - Predictive Maintenance
More companies are successfully exploiting predictive maintenance systems that combine AI and IoT sensors to collect data that anticipates breakdowns and recommends preventive action before break or machines fail, in a demonstration of an AI use case with proven value.
Kaiser Permanente Practicing Predictive Maintenance in Healthcare
Kaiser Permanente developed the Advanced Alert Monitor (AAM) system, leveraging three predictive analytic models to analyze more than 70 factors in a given patient’s electronic health record to generate a composite risk score.
“The AAM system synthesizes and analyzes vital statistics, lab results, and other variables to generate hourly deterioration risk scores for adult hospital patients in the medical-surgical and transitional care units,” stated Dick Daniels, executive vice president and CIO of Kaiser Permanente in the CIO account. “Remote hospital teams evaluate the risk scores every hour and notify rapid response teams in the hospital when potential deterioration is detected. The rapid response team conducts bedside evaluation of the patient and calibrates the course treatment with the hospitalist.”
In advice to other practitioners, Daniels recommended a focus on how the tool will be fit into the workflow of health care teams. “It took us about five years to perform the initial mapping of the electronic medical record backend and develop the predictive models,” Daniels stated. “It then took us another two to three years to transition these models into a live web services application that could be used operationally.”
Example from the food industry
PepsiCo Frito-Lay plant in Fayetteville, Tenn. is using predictive maintenance successfully, with year-to-date equipment downtime at 0.75% and unplanned downtime at 2.88%, according to Carlos Calloway, the site’s reliability engineering manager, in an account in PlantServices.
Examples of monitoring include: vibration readings confirmed by ultrasound helped to prevent a PC combustion blower motor from failing and shutting down the whole potato chip department; infrared analysis of the main pole for the plant’s GES automated warehouse detected a hot fuse holder, which helped to avoid a shutdown of the entire warehouse; and increased acid levels were detected in oil samples from a baked extruder gearbox, indicating oil degradation, which enabled prevention of a shutdown of Cheetos Puffs production.
The Frito-Lay plant produces more than 150 million pounds of product per year, including Lays, Ruffles, Cheetos, Doritos, Fritos, and Tostitos.
The types of monitoring include vibration analysis, used on mechanical applications, which is processed with the help of a third-party company which sends alerts to the plant for investigation and resolution. Another service partner performs quarterly vibration monitoring on selected equipment. All motor control center rooms and electrical panels are monitored with quarterly infrared analysis, which is also used on electrical equipment, some rotating equipment, and heat exchangers. In addition, the plant has done ultrasonic monitoring for more than 15 years, and it is “kind of like the pride and joy of our site from a predictive standpoint,” stated Calloway.
The plan has a number of products in place from UE Systems of Elmsford, NY, supplier of ultrasonic instruments, hardware and software, and training for predictive maintenance.