Predictive maintanance programs and continous monitoring of critical machinery prevent downtimes, better protect machinery and create huge savings. Oil & Gas and Power Generation industries were the first to adopt these practices. However, such programs are now gaining acceptance in every industry. The new tools and technologies that became available recently, also reinforce this trend. Model based fault detection technology is one of them. It simplifies condition monitioring at every level: installation, use and responsive action. It is already helping many plants deploy their predictive maintenance programs. And they are experiencing great returns on their investment.
Model Based Fault Detection Technology - Overview
Electric motors are not complicated systems mathmetically and generally, neither the systems driven by them. In other words, they are predictable. Any deviation from this predicted behaviour points to a problem, either a mechanical or an electrical one. The input and output measaruments are all what's needed to model any system, that is to learn its behaviour so that it can be predicted under certain inputs. In the case of an electrical motor driven system, only voltage and current readings suffice.
Model Based Fault Detection Technology can overcome some of the major challenges of asset condition monitoring.
Electric Motorand the Driven SystemCan BeMonitored Simultaneously It's because the learned model is not just for the electric motor but for the system as a whole. Especially, for systems that involve just a fan or a pump for example, the faults that relate to looseness of belt (for fans) or cavitation (for pumps) are also reilably detected. Sensors needed: the motor itself with its current and voltage readings. Underground and Underwater Motors become Reachable It's because the voltage and current readings of the motors can be obtained at the motor control center. These are the only measurements needed for modeling and then condition monitoring.
Prioritization of Motors for Maintenance Becomes Feasible It's because now you can see the severity of the developing anomolies in the motors on your desktop. You can run further tests on the ones that are showing severe anomolies. You can prioritze your maintenance actions during a scheduled downtime, on the motors that are more likely to fail soon because you are given an early warning. You can protect your motors and the driven machinery better, by being able to address the root causes of faults before they do extensive damage.
SCADA Systems Integration of Machine Condition Monitoring is Simplified It's because OPC is utilized and the bulk of the data processing is done at measurement points. No need to send streams of data continously to any remote central processing unit.
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