Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
The Machinery Fault Diagnosis and Signal Processing Certification course is a 12-week online program. The IIT Kharagpur is responsible for creating and approving the course syllabus, although Swayam is used to deliver the course. Once more, the Indian Institute of Technology, Kharagpur, and NPTEL are the organizations that award this course certificate.
The Machinery Fault Diagnosis and Signal Processing training is a part of the mechanical engineering elective curriculum. This course will give access to state-of-the-art equipment condition monitoring techniques along with the most recent advancements in signal processing, thermography, and ultrasonics.
Professor Amiya Ranjan Mohanty is the instructor of this Machinery Fault Diagnosis and Signal Processing certification course.
The Highlights
- Duration: Twelve weeks
- Virtual course
- Elective Programme
Programme Offerings
- Postgraduate Level
- assignments
- Certificate Of Accomplishment
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 1000 | yes | IIT Kharagpur |
The Machinery Fault Diagnosis and Signal Processing certification fees is free, but participants must pay Swayam Rs.1000 for the final exam if they decide they want the certification.
Fee Structure
Description | Amount |
Course Exam Fee | Rs.1000 |
Eligibility Criteria
Eligibility
Mechanical engineering
Educational Qualification
This Machinery Fault Diagnosis and Signal Processing Certification by Swayam is open to educators, business professionals, and students.
Machinery Fault Diagnosis and Signal Processing Certification Qualifying Details
The completion of Machinery Fault Diagnosis and Signal Processing is certified by IIT Kharagpur, Swayam, and NPTEL once the candidates receive 25% on 8 out of the 12 assignments and a 75% on the certification exam.
What you will learn
The students will be able to comprehend the real-time health monitoring of machinery through various condition monitoring aspects after completing the Machinery Fault Diagnosis and Signal Processing certification syllabus.
Who it is for
This course is ideal for any educator, business professional, and student who wants to become an engineer. Research analyst and maintenance engineer would be the specific roles.
Admission Details
To enrol for Machinery Fault Diagnosis and Signal Processing certification classes, the students may follow the steps:
Step 1: Follow the official URL https://onlinecourses.nptel.ac.in/noc22_me60/preview
Step 2: On the Swayam site, the participants will then have to register themselves.
Step 3: Participants won't receive admittance confirmation until they can register and log in.
The Syllabus
- Maintenance Principles
- FMECA
- Fault Prognosis
- Vibration Analysis
- Experimental Modal Analysis
- Rotor Dynamics
- Time domain Signal analysis
- Data Acquisition
- Filtering
- Fourier Series
- FFT
- Modulation and Sidebands
- Order Analysis
- Orbits
- Instrumentation
- Data Recording
- Vibration and Noise Monitoring
- Rotating Machines
- Bearings and Gears
- Fans
- Blowers
- Pumps
- IC Engines
- Motor Current Signature Analysis
- Wear Debris and Oil Analysis
- NDT
- Ultra-sonics
- Eddy Current
- Case Studies
- Failure Analysis
Evaluation process
Students must take the exam in person at one of the specified exam centers after registering.
Instructors
IIT Kharagpur Frequently Asked Questions (FAQ's)
Yes, the students will be given 3 points of credit.
Yes, this Machinery Fault Diagnosis and Signal Processing online course has been approved by AICTE.
Amiya Ranjan Mohanty is the professor teaching this Machinery Fault Diagnosis and Signal Processing online course on Swayam
The total marks assigned for the Machinery Fault Diagnosis and Signal Processing final exam are 100.
No. Hard copies of the certificates cannot be delivered to the residential address of the students.