COURSE OVERVIEW:

In the era of the Industrial Internet of Things (IIoT), engineers are increasingly inundated with vast amounts of data from sensors, control systems, and maintenance records. This course is designed to bridge the gap between raw data collection and actionable engineering insight, providing a toolkit of analytical techniques specifically for technical professionals. Participants will explore how to apply statistical methods to interpret process trends, machinery health, and structural integrity, transforming numbers into decisions that improve plant performance.

The scope of the Coverage includes both descriptive and inferential statistics, with a heavy emphasis on practical application rather than abstract theory. We will examine how to clean and validate "noisy" industrial data, identify outliers that signal impending equipment failure, and use correlation analysis to understand the relationship between different process variables. Attendees will learn how to use visual analytics to communicate complex technical findings to non-technical stakeholders, ensuring that data-driven recommendations are clearly understood and implemented.

Furthermore, the course introduces predictive modeling and reliability data analysis, including Weibull distribution for understanding failure patterns. By mastering these techniques, engineers and technologists can move beyond simple averages to more sophisticated analyses of variance and regression. This enables more accurate forecasting of equipment life, optimization of process parameters, and the identification of root causes for chronic technical issues that traditional troubleshooting might miss.

COURSE OBJECTIVES:

After completion of this course, the participants will be able to:

1. Apply fundamental statistical concepts to engineering data sets.
2. Clean and preprocess raw sensor data for accurate analysis.
3. Identify and handle outliers and missing values in technical records.
4. Perform correlation and regression analysis to find process dependencies.
5. Use descriptive statistics to summarize equipment performance trends.
6. Interpret probability distributions to assess the likelihood of failure.
7. Apply Weibull analysis to determine equipment wear-out characteristics.
8. Conduct "Hypothesis Testing" to validate the impact of technical changes.
9. Create effective data visualizations for technical reports and presentations.
10. Utilize Control Charts (SPC) to monitor process stability.
11. Perform Root Cause Analysis (RCA) using data-driven methodologies.
12. Forecast future maintenance requirements using time-series analysis.
13. Leverage data analysis to optimize spare parts and resource allocation.

TARGET AUDIENCE:

Process Engineers, Mechanical Engineers, Reliability Technologists, Maintenance Supervisors, and Quality Control Professionals who need to analyze technical data.

TRAINING COURSE METHODOLOGY:

A highly interactive combination of lectures, discussion sessions, and case studies will be employed to maximise the transfer of information, knowledge, and experience. The course will be intensive, practical, and highly interactive. The sessions will start by raising the most relevant questions and motivating everybody to find the right answers. The attendants will also be encouraged to raise more of their questions and to share in developing the right answers using their analysis and experience. There will also be some indoor experiential activities to enhance the learning experience. Course material will be provided in PowerPoint, with necessary animations, learning videos, and general discussions.

The course participants shall be evaluated before, during, and at the end of the course.

COURSE CERTIFICATE:

National Consultant Centre for Training LLC (NCC) will issue an Attendance Certificate to all participants completing a minimum of 80% of the total attendance time requirement.