FARADAY PREDICTIVE

Rotary Equipment Condition Monitoring

Faraday Predictive’s MBVI system is an innovative predictive maintenance platform that enables industrial equipment to be monitored for early warning signals of potential failures. The platform allows for automated fault diagnosis of motor and motor-driven equipment by analyzing voltage and current signals. This advanced technology can detect a wide range of faults, including generic issues such as bearing problems and cracked rotor bars, as well as major faults like pump cavitation, unbalance, misalignment, and foundation looseness. This technology helps maintenance teams take proactive steps to prevent unplanned downtime and optimize the overall performance of the equipment.

Download PDF

P100 Portable

Equipment Assessor

On-Site

Diagnostic Survey

S200 Switchroom Mounted Condition Monitoring System

One-Site

Installation Survey

Technological Advantages

Predicts mechanical and electrical

faults up to 3 months in advance

Enable maintenance teams to schedule maintenance and repairs optimally, reducing the likelihood of costly breakdowns and equipment failure.

Sensors are installed in the MCC

Suitable for monitoring equipment that is usually inaccessible, submerged, or located in remote areas, such as submersible pumps. These sensors allow contractors to remotely monitor equipment performance and detect potential issues before they escalate, improving the reliability and safety of the plant.

Calculates the energy wastage caused by each fault

Enable contractors to prioritize repairs based on their potential impact on energy consumption, reducing wastage and improving the overall efficiency of the water treatment plant.

Does not require frequent recalibration

Does not require frequent recalibration

Reducing the need for frequent site visits and the overall cost of ownership for contractors, making Faraday Predictive a cost-effective and reliable solution for monitoring equipment performance.

Minimize downtime and maximize efficiency with Faraday Predictive.