Predictive Maintenance Data Acquisition Engineer

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#Program

About Program

NOS Name: Predictive Maintenance Data Acquisition Engineer
NOS Code: CSC/Q
NOS Version: 1.0
NSQF Level: 6
Model Curriculum Version: 1.0

A Predictive Maintenance Data Acquisition Engineer is responsible for designing, implementing, and managing data collection systems that enable predictive maintenance in industrial environments. This role involves integrating sensors, data acquisition hardware, and analytical software to monitor equipment health, detect anomalies, and prevent unexpected failures. Predictive maintenance plays a crucial role in modern industries by minimizing downtime, reducing maintenance costs, and optimizing asset performance.

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Module

Module 1

Introduction to Predictive Maintenance Data Acquisition

Module 2

Designing Data Acquisition Systems

Module 3

Implementing Data Acquisition Systems

Module 4

Analyze the Data Acquisition Systems

Module 5

System Maintenance Using Data Acquisition Techniques

#Overview

Program Overview

Training Outcomes
At the end of the program, the learner should have acquired the listed knowledge and skills.

–  Understanding Predictive Maintenance Concepts: Grasp fundamental concepts and
methodologies of predictive maintenance and its role in asset management.
–  Data Acquisition Techniques: Master techniques for collecting data from various
sources, including sensors, IoT devices, and equipment interfaces.
–  Signal Processing Skills: Develop skills in signal processing methods to analyze and
filter data for meaningful insights.
– Data Analysis Proficiency: Gain proficiency in data analysis tools and software (e.g.,
Python, R, MATLAB) to extract relevant patterns and trends.
–  Familiarity with industrial automation and control systems, including PLCs, SCADA
systems, LIMS, Machinery Monitoring and Protection Systems, Data Historians and
DCSs.
–  Understanding of cybersecurity principles and best practices to ensure data privacy
and security.
–  Ability to work collaboratively with cross-functional teams, including engineers,
maintenance technicians, functional/domain experts and data scientists.
–  Familiarity with industry standards and regulatory requirements related to predictive
maintenance, such as ISO 20816, ISO 18436, ISO 13379, ISO 15531, ISO 13381.
–  Knowledge of project management principles and practices, including project
planning, scheduling, and budgeting.
–  Sources of various data, methods of integration of various systems and collection of various data.

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#TrainerInfo

India's Leading Trainers.

Mehek Gunjan

IIT Delhi

Kailash Biswa

IIT Kanpur

Debika Roy

Harvard University

Praful Modak

IIT Bombay

Course Ingredients

Skill Development
Time Management
Work Management
Real Life Example
Career Support