OSU College of Engineering, Professional & Distance Education Programs Store

Predictive maintenance (PdMS) is not a tool, technique or certification. Predictive maintenance is a philosophy that uses the equipment’s operating condition to make data-driven decisions and improve quality, productivity and profitability. Unlike industry courses that focus on applying specific technologies like vibration monitoring or oil analysis, this course focuses on establishing, managing and sustaining results from a comprehensive PdM program.

Explore how to improve asset availability and meet reliability goals by applying a risk-based approach to asset maintenance and operations. In the Risk-Based Asset Management (RBAM®) course, you practice how to prioritize reliability efforts on critical equipment and failures that impact your operation. RBAM incorporates reliability-centered maintenance (RCM) principles and continuous improvement practices like PDCA to position your program for decreased downtime, lower maintenance expenditures, and an acceptable total cost of ownership.

In this 3-day course, participants build a work management program based on Reliability Excellence best practices. The course is designed for a Lead Planner or Department Manager to establish a proactive work management program or to transform an existing program into an effective, results-producing department that positively impacts maintenance costs, plant reliability measures and employee morale.

In this three-day program, participants will build an action plan for managing their human capital while developing an effective program for managing assets. Supervisors will learn how to leverage their personal supervisory style, apply time management techniques, run effective meetings and improve maintenance delivery.

Learn how a Reliability Engineer (RE) drives the value assets can deliver by overseeing equipment life cycle performance from concept through disposal. In Reliability Engineering Excellence, REs learn to build a business case for reliability, design reliability into a system or process before it’s built, identify operating risks and solve problems in all areas of asset management. Life Cycle Institute reliability experts facilitate class activities around system reliability modeling, ISO 55000-based assessment questions and how to use leading and lagging indicators to manage a reliability program.

This three-day course examines topics including standard work processes, job estimation and prioritization, backlog management, job plan development, work scheduling and coordination. Participants engage in hands-on activities that build system-independent knowledge and skills they will be able to apply immediately.

Neural Networks and Deep Learning is one of five non-credit courses in the Certification in Practice of Data Analytics (CPDA) program. This course can be taken individually or as one of four courses required to receive the CPDA certificate of completion. Neural Networks and Deep Learning can be taken after Statistics, Data Mining, and Machine Learning in the CPDA program.

Municipal Planning and Zoning Fundamentals introduce students to city planning concepts, municipal governance, and public service. Offered by the City and Regional Planning Program at The Ohio State University, this training prepares local leaders to guide their municipalities and promote civil discourse in their communities.

Visual Analytics for Sensemaking is one of five non-credit courses in the Certification in Practice of Data Analytics (CPDA) program. This course can be taken individually, or as one of four courses required to receive the CPDA certificate of completion.

Learn about green building methods, techniques and practices and how they can be applied to planning practice. This course focuses on concepts associated with Leadership in Energy & Environmental Design®, (LEED®), that recognizes building practices that save money, reduce water consumption, improve indoor air quality, make better material choices, and overall have less negative impacts to the environment.

Introductory Statistics for Data Analytics is one of five non-credit courses in the Certification in Practice of Data Analytics (CPDA) program. This course can be taken individually, or as one of four courses required to receive the CPDA certificate of completion. If you pursue the entire certification, this course must be taken first in the sequence.

Applied Machine Learning is one of five non-credit courses in the Certification in Practice of Data Analytics (CPDA) program. This course can be taken individually or as one of four courses required to receive the CPDA certificate of completion.