Imagine artificial intelligence (AI) could automate data analysis to create prescriptive and prioritized maintenance tasks. What if AI could automatically create a work order in existing computerized maintenance management systems (CMMS)? Imagine automating data collection through the internet of things (IoT) so the user experience is cost-effective and scalable.
Sound too good to be true? Believe it or not, it is possible.
Where to Start
Typically, there are three major categories of conditions that lead to machinery failure:
- Late-stage conditions: imbalance, misalignment, bent shaft, soft foot, looseness, etc.
- Early-stage conditions: lubrication, early-stage bearing failures
- Intermittent conditions: cavitation, resonance, speed related, process related
For late-stage conditions, an AI model views the frequency band from 2 to 1,000 Hertz (Hz). The AI compares the amplitude of the vibration to a set of ISO 10816 standard alert limits and generates an appropriate alert based on the severity (how much amplitude is in the vibration). Using the ISO-defined and industry-accepted alerts of 0.2, 0.5 and 1 inch per second (IPS) peak, the AI rules engine sends an alert via text message or email to the user.
For example, when a 0.2 IPS threshold is crossed, the automated prescriptive maintenance task is to “field inspect” the machine using human experience and senses of sight, sound, touch and smell. When an alert condition is present, human senses are often successful in finding the root cause of equipment issues. Remote monitoring of spectrum data, although valuable, is no substitute for physical presence at the asset to inspect equipment and communicate with operations about its history and context.
Diagnosing Early Stage Conditions
For early stage conditions, an AI model watches the frequency band from 1,000 to 30,000 Hz. This range includes ultrasonic frequencies and compares the amplitude of the ultrasonic measurement to a set of established alert limits. Ultrasonic or high-frequency monitoring is relatively new and ISO or industry standards are limited. Using experience and industry-proven alerts of 6, 12, and 18 Gs (force of gravity), the AI model sends an alert via text message or email.
For example, when plant assets are operating between 600 and 6,000 rpm and a 6 Gs threshold is crossed, the automated prescriptive maintenance task is to field inspect and lubricate. Again, human experience and senses are used to collect information relating to the cause of the alert condition.
Tips for the human inspection include:
- asking operations about any changes or insights
- looking or smelling for smoke or hot sensations or unusual odors
- looking for leaks, spills or broken or loose supports
- listening for rubbing or grinding
Some maintenance technicians use a stethoscope to listen to bearings to compare a good-sounding bearing with a suspect one. Using an infrared (IR) temperature gun or camera to inspect for hot spots is another helpful technology. The user may also check lubrication levels and color in sight glasses, or for signs of recent greasing. Then, they determine if it is a greaseable bearing, if it is under- or over-greased, if the wrong grease was used or if it was auto-greased. If it is an oil bath bearing, they will also check the oil bath level. They will determine if the machine is a chronic problem, if it is running at design or if it has been modified, sped up, load increased, belt tension increased, etc.
As always, employing an experienced vibration analyst is a safe bet. However, in midtier plant and/or with midcritical assets, a hybrid human and AI program can be successful.
This is where continuous monitoring with permanent or temporary monitoring stands apart from a monthly route-based program. With wireless sensors and the IoT, coupled with the AI models described above, intermittent conditions can be monitored and root issues can be quickly identified and resolved.
A common reality of vibration analysis on midcritical assets is they are typically monitored monthly, quarterly or not at all. In the route-based predictive maintenance program, the analyst gathers data monthly, taking 10 or more points per machine, and conducts a brief inspection of the machine. In contrast, continuous monitoring gathers fewer measurement points, and the data is logged and reviewed remote from the machine, in a control room, plant office, by remote third party, or all of the above.
Through continuous monitoring with low-installed-cost IoT sensors, many issues that have perplexed the monthly routine are resolved. Examples vary from boiler feedwater pumps that blow a seal on the night shift, to kraft pulpers that crater a gearbox over the weekend, failed pump seals related to misalignment from thermal growth, or inconsistent lubrication practices. Intermittent issues are best solved with continuous data coupled with AI to set alert limits.
These systems typically include a sensor, cellular connectivity and AI in the cloud.