What Is Sensorlytics?

Sensorlytics is the evolution of wearable sensor technology commingled with powerful analytics algorithms that predict future machine performance and alert users to machine issues prior to failure.

Sensors have been around for years and years, but most industrial sensors have been expensive and lacked machine analytic capabilities. So they simply reported temperature, pressure, vacuum, or other stats—and then relied on operator observation to do something about the problem. However with the advent of Technology 4.0 and the tremendous reduction in costs of these sensors as well as miniaturization of them, wearable sensor technology is now becoming mainstream for industrial equipment. Much of this technology has been led by wearable technology for humans (fitness trackers, etc.) that measure and report vital signs. Likewise, wearable sensors for machinery also report the vital signs of equipment and log that data ongoing so that it can compared to baselines and trends and reliably predict maintenance performance or pending failure.

The analytic part of Sensorlytics is the gathering of the data signals from the machine systems on an ongoing basis and then using powerful rules-based software, combined with the power of server technology, to compare that data constantly for exceptions that can be reported either to the plant maintenance personnel or to service providers.

The analytic part of Sensorlytics is a critical component to machine wearable sensor technology in that machines often do not have “voices” or “brains” to interpret the data and rely instead on operator observation—which is usually focused directly on production requirements. Unlike humans who use wearable technology and see an abnormal heart rate, blood/pressure or glucose levels and then see a doctor for maintenance, the machine typically keeps running to failure as its conditions get worse and worse. And, when the machine fails, the production line is down and plant management is often surprised when there seemed to be no reason for failure. So, Analytic Technology 4.0, patented by Prophecy, helps predict machine performance based on vital signs to help processors schedule maintenance wisely rather than waiting for critical line stops.

A good example of this is the simple vacuum conveying pump found in so many plastic facilities today. The pump provides the air flow to convey plastic pellets from a storage container of some type to processing machinery. Without the flow of those pellets, you can’t make a final plastic product. So, in essence, the pump is the “heart” of the system. Its continuous heart beat is critical to providing production. That pump typically has a motor, a blower, belts, sheaves, even a gearbox in some cases. All these are mechanical devices that are subject to failure and the failure mat y seem to be unpredictable. However, by using wearable sensor technology and analytics we can monitor such things as motor temperature, blower vibration, oil pressure variance, and other variables that help us see deviance from baselines and predict failure if steps are not taken proactively. Impending failure usually shows up in steadily increasing vibration or higher temperatures all of which are being monitored real time so that you lower the risk of shutdown during critical production times. Of course, this doesn’t remove the need for maintenance, but Sensorlytics helps you plan that maintenance and avoid unplanned shutdowns. Surprises can be a thing of the past...

Sensor data can be accessed On phone, tablet or computer.

Irregularities in performance are tracked vs. established baselines.


In order to truly capture the full optimization potential, you not only have to know the current state of your operating equipment, but you need the ability to prophecize or predict what will happen before it actually happens. While most machinery with controls and some sensors have always issued early warnings or issued alarms, they were usually inconsistent and not predictive. The advent of embedded lower cost sensors and networked machines as well as advanced analytic software has changed that paradigm.

Now processors can leverage the analytics to predict machine issues, providing real-time intelligence to processing management—not just maintenance management—so that best decisions can be made about scheduling maintenance and which assets to utilize. Management, for the first time, will have the proper foresight to take the right actions at the right time to avoid issues before they occur—allowing for more continuous operation and less discontinuous operations…a real breakthrough in productivity and profitability for most processors.