Idea Details

Team and Contact Details

SNo Student Name School Degree Year Email
1Fahad IftikharPNECUndergraduateFourthfahad.ee@pnec.nust.edu.pk
1Hasan JalalPNECUndergraduateFourthhasan.ee@pnec.nust.edu.pk
1Furqan NavaidPNECUndergraduateFourthfurqan.ee@pnec.nust.edu.pk
1Muhammad AliPNECUndergraduateFourthali.ee@pnec.nust.edu.pk

Inter School Idea ? No
Do you need expertises from another area: Yes
If Yes please provide details of expertises you need: SIGNAL PROCESSING AND COMMUNICATIONS

Idea Details

Idea Name: Machine Health-Monitoring
Slogan: No more industrial shutdowns
Supervisor Name: Dr Syed Sajjad Haider Zaidi
Supervisor Designation: Assistant Professor
Supervisor School: PNEC
Supervisor Department: Department of Electronics and Power Engineering
Contact number: 03213888853
Email ID: sajjadzaidi@pnec.nust.edu.pk
Abstract:
    Fault in an induction motor in an industry can cause major downtime and loss of millions of dollars. Our project carries out health monitoring of an induction motor in real time to deal with them.
What is the unmet need in society that your idea will fulfill ?
    Almost 90% of industrial drives are of induction motors. A fault in any of them reduces the efficiency of the plant, single point failures and huge monetary losses. We will provide condition based monitoring of induction motors to rectify this issue.
Who needs it ? How many would benefit ?
   The role of induction motors in industries is wide and common. It finds its application in all places where induction motors are part of a system to carry out important operations. Hence health monitoring of machinery is essential and has high demand in the market.
How will the solution works
    It involves data extraction through current, vibration and temperature sensors integrated with the motors. The data fetched is fed through a circuit and DAQ to LabVIEW which stores the data. This process is repeated with different motors with a specific fault and different datasets are made. Finally, MATLAB is used for signal processing which compares the datasets obtained from healthy motors and unhealthy motors and then predict the remaining useful life of the motor or any faults present.
Who are your competitors ? How is your solution different
    Currently, there are no competitors in the market. We aim to replace the traditional preventive based monitoring with condition based monitoring system fully applicable for industrial needs once tested in the lab.
Status: Approved
Entry Date & Time: 2018-12-23 (1754)