Machinery Lubrication magazine published by Noria Corporation
Issue link: https://www.e-digitaleditions.com/i/1222953
www . machinerylubrication.com | March - April 2020 | 9 B ig data analytics, de e p m a c h i ne learning, artif i- cial intelligence and the rise of the algorithm are driving the newest industrial revolu- tion and have been for quite some time. e ability to capture data, create digital twins and autono- mously alter, optimize and even control a machine's performance from the other side of the world is now part of the fabric of many companies and industries. In short, everyone now strives to be "digital." However, for all this talk of digital and Industry 4.0, the manner in which industry monitors arguably the most crit- ical part of any machine — its lifeblood, its lubricating oil — still pre-dates this new digital movement and largely entails someone physically taking a sample of the oil, placing it in a plastic cup, handwriting the details of the oil and the machine, putting the sample in an envelope, sending it to a laboratory and waiting patiently (sometimes weeks) for a result showing the status of the oil at that small snapshot in time. Alternatively, a sample may be taken and tested locally using either a testing kit or a machine, giving a less exhaustive single snapshot. This article will identify how the lubricant monitoring industry has started to change this paradigm as well as spec- ulate where the industry is going and the potential it has to fully embrace Industry 4.0 and the digital revolution. DATA IS USELESS e push for "digital" relies heavily on the data that a system can ingest, and thus "data" has become very much in vogue. Now, there is almost an obsession with data, but by itself, data is completely useless. I personally own a well-known brand of fitness tracker watch. is fitness tracker has a wonderful feature that tracks my sleep. When I wake up, I almost obses- sively check my phone, download the data and look at my sleep pattern. It can tell me how long I slept, whether I woke up during the night and even how deeply I slept over the course of the night. e analytics appear to be sound, and even the data is presented in a user-friendly manner. So, what does this data mean? What do I do with all this information? What changes do I make based on the data? What outcomes are derived from this sleep-tracking feature? None. Zip. Zero. It's purely useless data. Data falls into two categories: what can it tell me and what do I need to know? In this case, the data falls under the first category. Data that doesn't drive outcomes is useless data. For data to be meaningful, it must follow four rules: analysis, interpretation, context and outcome. A NA LYSIS — The data must be fundamentally sound. Raw numbers do not provide meaning. e raw data must be turned into a useful output. INTERPRETATION — What does the data mean? How do the numbers relate to the reality of a situation? CONTEXT — is element is largely ignored yet provides the most important insight. Context of the conditions surrounding the data can be as important as the data itself. OUTCOME — is is another often ignored feature of data. As per the sleep tracker example, if data doesn't drive a clear outcome, then it has no meaning. Consider measuring a person's weight. Let's say a man weighs himself, and the (digital) scale shows that he is 184 pounds. Is this good? Is this a healthy weight? At this point, you only have analysis. Without the other three elements, this data is useless. For interpretation, let's say you compare his weight to a weight/height chart. By this interpretation, 184 pounds for a man who is 6-foot-3 is a normal weight. is usually is where the data investigation ends, with analysis and interpretation, the correlation of single numbers versus specifications, and basic interpreted results. Green, yellow and red results are normal in industry, espe- cially in oil monitoring reports. What's missing are the context and outcome. Without context, you are ML