The Maintenance Standard

Predicts The Failure Before They Happen.


About Us

We are a team of reasearchers and developers at NED University of Engineering & Technology.We are working on the technique that uses data analysis tools and techniques to detect anomalies in your operation and possible defects in equipment and processes so you can fix them before they result in failure.
It helps in:

  • Minimizing the time the equipment is being maintained
  • Minimizing the production hours lost to maintenance
  • Minimizing the cost of spare parts and supplies

When predictive maintenance is working effectively as a maintenance strategy, maintenance is only performed on machines when it is required. That is, just before failure is likely to occur. This brings several cost savings and It answers three questions:

  • Is your machine operating normally?
  • Why is your machine behaving abnormally?
  • How much longer can you operate your machine ?
Learn More

Predictive Maintenance Cycle

  • 01 Acquire Data

    Collecting historical data about the machines’ performance and maintenance records to form predictions about future failures. Usage history data is an important indicator of equipment condition.

  • Cleaning the data and convert it into a form from which we can train our model.

  • Condition indicators are the features in your system data whose behavior changes in a predictable way as the system degrades. A condition indicator can be any feature that is useful for distinguishing normal from faulty operation or for predicting Remaining Useful Life (RUL).

  • To do predictive maintenance,sensors will be added to the system that will monitor and collect data about its operations.Training model on your data, as per your requirement.

  • Deployment or integration of a predictive maintenance algorithm is typically the final stage of the algorithm-development workflow.


Project Progress

Data Accusition 90%

Cloud Server95%


Model Training80%

Future Results

Our targed sections to satify our users will be:

Alarms, Hand held devices

Maintenance needs simple, quick information .

Integrated IT and OT Systems

That will be provide operations a birds-eye view

Automated Reports

It is important that customers have easy access to information.

Our Team

Dr Majida Kazimi

Principal Investigator

Dr Arshad Aziz

Co-Principal Investigator

Dr Hashim Raza

Co-Principal Investigator

Lubaba Rehman

Research Associate

Shabeeh Abbas

Research Assistant

Maria Shoaib

Research Assistant

Our Industrial Partner

Our Commercial Partner

Our Research Partner

Contact Us

Our Address

Neurocomputation Lab, NCAI Computer and Information Systems Department NED University of Engineering & Technology University Road, 75270, Karachi.

Email Us

Call Us

+92 219 9261203