Mr Orenstein, how do manufacturers see the tsunami of sensor-generated data?
Horia Orenstein: “Let me start by saying that my experience with sensor-generated data mainly stems from the petrochemical industry. In such a process-driven environment, sensors and meters are both a blessing and a curse: a blessing because they give a 360° view of the process at any one time, and a curse because they’re expensive and – more importantly – there are not enough means to transform the data into actionable information.”
How can data analytics help here?
Horia Orenstein: “When you consider a process, it’s of the utmost importance to be able to detect and even predict possible deviations and process disruptions, such as trips. And it’s just as crucial to understand the main contributors to the deviations leading to critical disruptions! You would need some kind of smart meter or sensor that can ‘look’ inside the processing units. You have to constantly monitor these parameters and predict the risk of a failure. That’s where we apply analytics. A more complete set of measurable parameters helps to ensure better measurement of process efficiency in real time, in both technical and economic terms, and also to forecast the life expectancy of equipment, processing units and entire plants. One key point here is that when deviations from expected values occur, the most significant contributors are reported together with the deviations, so that operators, engineers and managers understand the problem instantaneously and can take appropriate corrective action.”