In the world of condition-based maintenance for many years, there has been prejudices between different styles of analysts promoting their technology/style as the most effective in regards to indicating certain conditions that lead to machine failure over various industries and machinery. For the sake of this article we are going to focus on Lubrication and Vibration Analysis.
The Nature of Lubrication Analysis
Traditionally if you were performing analysis on your machine’s lubrication or fluid, you might send a sample to a lab to take a single snapshot of the state of the lubrication in that moment. In the past this has been adequate and has been being performed over the last 30 years. Once the lab receives this sample, they will send their analysis back to you within 7-14 days. Great! This report holds a ton of important information that would be very helpful in understanding the health of your machine and the components in which make it. The only problem? You would have to be trained in fluid analysis to even understand this report. With this you have two problems – one, it was only a small snapshot of the condition of the oil in that time, and two it is very difficult to interpret the report as an effective way for preventative/predictive machine maintenance.
What Can Lubrication Analysis Detect?
- Detect Particles | Protect Teeth
- Detect Changes in Heat | Spot Varnish Creation
- Changes in Viscosity | Possible Cavitation
- Changes in Moisture | Density
- Detect Additive Levels
The Nature of Vibration Analysis
As you can imagine there are also challenges with vibration analysis, not in the terms of the effectiveness of the technology or the accuracy of the sensors, but just from the nature of how it is collected and analyzed. Similar to lubrication analysis, if you are implementing vibration analysis on your machinery to detect specific events that can help prevent further damage to machinery you are likely to hire this out. A company will come in and deploy vibration sensors in various locations. If you were to attempt interpreting the data from these sensors you would be hard-pressed to gain any value from it unless you had some reasonable training on the subject.
What Can Vibration Analysis Detect?
- Misalignment of coupling bearings, and gears
- Unbalance of rotating components
- Looseness of bolts, grouting or excessive clearance
- Deteriorating rolling elements in bearing
- Gear wear
- Aerodynamic/Hydraulic forces
- Electrical problems such as imbalance in motor
- Eccentricity of rotating components
Wouldn't it be nice if there was a way to somehow use both of this data together to provide more powerful insight into my machinery?
How the IIoT Changes All of That
So let’s summarize. Lubrication analysis hasn’t had much innovation in terms of uncovering what the data means, and vibration analysis seems very isolated from other types of sensor data. They both seem very isolated! You can imagine that neither the lubrication analysis lab or the vibration analysis company knows anything about how to interpret the other one’s data or have the ability to easily overlay both pieces of data. But wouldn’t you think that they should be used together? They are both needed as they are monitoring critical assets of the machine that can help tell a bigger story. There is a definite hole in the marketplace for “flexible” data sharing. What do we mean by flexible? There are many wireless devices on the market now that go directly to the cloud or to an onsite server. Either way, they are typically running a proprietary software package. There are also several wired devices that do the same thing. What the IIoT provides is the ability to connect to other data sources, databases and dashboards while still providing the traditional diagnostic information being provided by lubrication and vibration analysis sensors.
Both, the lubrication and vibration world still struggle integrating their data with other machinery health data sources and the process control data. The IIoT bridges that gap. It provides the ability to easily combine and connect with other data sources, both ones from the existing sensors, and third-party data sources (labs). Long story short, the IIoT provides some context to the data and integrates well into other systems.
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