Predictive maintenance in the Industrial Internet of Things

What is Predictive Maintenance? Result: switching from schedule-based or cycle-based maintenance to more cost-effective condition-based maintenance *Condition-based maintenance can be more frequent than schedule-based one. In this case, you benefit from avoiding equipment failures Component #1: remote monitoring Component #2: trends and patterns Component #3: learn what is good and what is bad Component #4: use automatic learning

- 6 - Maintenance Programs

- 6 - Maintenance and Repair Costs

Remote Monitoring Success of equipment condition analysis depends on data visibility, i.e. having a full set of data Complex equipment units have embedded sensors and special modules (agents) providing connection to the monitoring center Monitoring is often not an option as it solves disputes between vendors and users Agent is autonomous and able to work in non-operational mode (e.g., during transportation) Examples: data center equipment, commercial line aircraft engines, tomographs

Manual Learning In most cases using Big Data and storing raw data is unnecessary and over-priced All you need is a simple linear trend based on statistics by periods Examples: disk space running out, leased network channel utilization Negative patterns are normally complicated, and can be revealed only by a group of experts after in-depth data analysis Example: black box analysis after a plane crash due to technical failure

Automatic Learning From all the countless Machine Learning capabilities, only trends analysis is used often Generally, finding a negative pattern in a certain trend is enough (example: failure in the refrigerator compressor operating cycle) In more complicated cases, correlation of several trends is required («if this metric changes in this way and that one in another way, then everything is bad») It is possible to conduct learning based on data recorded earlier, not in real time

What Do We Get As a Result? Time to Failure (TTF) prediction Remaining Useful Life (RUL) estimation These metrics are easily converted from hours to operating cycles, mileage, transaction count, and other metrics TTF and RUL estimation is hard work, but it's proven to work well for rotating machinery, such as fans, pumps, and engines.

Equipment Condition Diagnosis Methods 1.Diagnosis during an operating cycle 2.Diagnosis via applying nondestructive testing during scheduled maintenance The first option is becoming more and more popular as sensors and industrial IoT technologies evolve. Diagnosis technologies: infrared and acoustic testing, vibration analysis, noise-level measurement, oil analysis, performance parameter evaluation, etc

Other Capabilities of Proactive Maintenance Systems Predictive maintenance usually includes Computerized Maintenance Management System (CMMS) Another additional function is maintenance logistics Equipment condition analysis often results in changing regulating algorithms For this purpose, centralized firmware update and readjustment are applied with the help of IoT Platform - 6 -