Machinery Lubrication magazine published by Noria Corporation
Issue link: https://www.e-digitaleditions.com/i/1103301
4 | March - April 2019 | www . machinerylubrication.com AS I SEE IT for mutual benefit related to machine performance and reliability. Machines with autonomous control features (current or potential) might include hydraulic systems, compressors, paper machines, turbines and many sophisticated process machine trains. e concept of coupling condition control with system control is illus- trated in Figure 4. Machine- and Human- Executed Responses Of course, not everything must be done in real time. Because of the complexity of some machines and the technology limitations of many condition and oper- ational control functions, both human and machine responses are needed. e IIoT and online sensors can supply the data, while data analytics can translate the data into prescriptive responses. However, the manner and time element of the corrective responses may vary. is hybrid model probably makes the most sense, as it is the easiest to deploy. But this is a dynamic field that will continue to evolve as technologies advance and machines become smarter and more agile. Examples of how humans and machines can work together are shown in Figure 5. The Internet of Tribology Oil is like a flight data recorder. It is exposed to the intimate innerworkings of the machine, seeing both the good and bad. It's the common medium that records data from these exposures which might reveal health or aberrant conditions that can induce future failure. Decades of research in tribology and millions of oil analysis samples have taught us that there's gold in our oil. e data that can prescribe needed actions is this gold. It is detectable and quantifiable. e means of data acquisition should not only be limited but also multimodal. It can be extracted from samples and analyzed in the laboratory, monitored in real time with online sensors, interrogated using Control Algorithm Action Response Feedback & Parameter Monitoring Data-to-Operator Avoidance Human-Executed Remedy Machine-Executed Remedy Data-to-Machine Avoidance Sensors & Transducers Shared Operating Limit Settings, Duty Cycle, Loads, Application, Operator Handling Filter Change. Component Replacement, Fluid Change, Alignment/Balance Correction Additive Discharge, Leak Isolation, Derate Load Changes to: Speed, Load, Acceleration, Temperature, Lubricant Supply, Auxiliary Filtration Root Cause Avoidance RESPONSE MODE Problem Remediation Human Executed Machine Executed Self-Referencing Knowledge & Edge Computing Shared SYSTEM (PLC) CONTROLLER CONDITION CONTROLLER Machine Control Actions/Condition Responses Condition Monitoring Condition Analysis Condition Response Figure 4. The Intelligent Controller-Controller Interface (ICCI) System shares PLC functions/sensing with machine condition functions/sensing. Figure 5. This chart shows how the IIoT provides connectivity for both machine and human executive condition control responses. MODES OF IIoT DATA-DRIVEN CONDITION RESPONSES