Hello, the Eduardo Schumann here and I'll be your guide through this Certified predictive maintenance
manager program. I'm going to start with a general program overview.
This course was designed for maintenance production for professionals,
building on foundational maintenance concepts to achieve high efficiency and
a world class sustainable results through predictive strategies. This course structure
progresses logically to the stages of maintenance maturity with a strong emphasis in
on-leveraging data and advanced technologies for effective predictive maintenance.
aligning with the principles outlined in my book, the Work class maintenance
playbook, something that was tested over the years and works for any manufacturing
company. A little bit about me
on top of being the Outer of the Work class maintenance playbook, I was an executive for
operations and maintenance for several companies. I have more than 35
years of progressive experience. I'm known for transforming operations and delivering sustainable results
across diverse global manufacturing environments. I already did the more than 200
projects worldwide, delivering more than six million
dollars in benefits hitting the EBITDA line. I am also a frequently invited
lecture and keynote speaker at the industry forums.
Trying to summarize what participants will learn in this course
is this chart that I've developed from some time ago. This shows
how to move from the maturity from five fighting to process oriented and
also, all the criticality because we don't want to use all the technologies
for any equipment, we want to be selective. And it is a way to explain a
little bit along the journey. But what might seem to be a
bit complicated is very moment. We're going to demonstrate and this plan step by
step to make sure that everybody understands what we are trying to achieve here.
And the importance of predictive maintenance in the role of this chart.
What I expect you to learn along this course is that a bit
of the understanding of predictive maintenance, principles, grasping the core concept of
detecting degradation before a failure to prevent downtime, especially
on critical equipment. Sensual technology, gaining knowledge on the application
of various sensors, like vibration, temperature, pressure, current oil conditions, acoustics, autosonic,
cavitation, electrical signals, for data collection, analysis, and more important
trends. We want to give a little bit of proficiency
in data acquisition, developing skills in continuous collecting and transmitting
data from sensors to a monitoring system, data analysis for patterns and trends,
acquiring ability to analyze, collected data for patterns, trends, and usual
variations in anomalies that indicate potential issues.
An anomaly detection is queuing the defined variations from normal operating
patterns that might signal imminent equipment problems, managing alerts and notifications,
understanding how to configure, ineffectively respond to automated alerts
generated by predictive maintenance systems, conducting faith on elimination stories,
familiar and spoff, using tools like failure mode and effect
analysis and single points of failures to identify root causes of failures and/or risks
and further opportunities for reliability improvement. Developing reliability
improvement strategies, learning to formulate and implement strategies, aim at enhancing
the overall reliability of the equipment. Optimizing preventive
maintenance intervals, safely extending the time between preventive maintenance tasks,
based on insights, gain it from predictive data and thinnet studies. Understanding
equipment criticality, preurizing predictive maintenance, efforts by
focusing on the most critical equipment, which accounts for a significant portion of the maintenance
issues. Leveraging online monitoring systems, applying online monitoring systems
for convenience, a surveillance of the critical assets, applying AI and machine learning
in maintenance, understanding how artificial intelligence and machine learning can help analyze
complex data to recommend the specific actions to prevent failures.
Graspi, digital twins concepts, understanding the foundation role of digital
twins in advanced data-driven maintenance strategies. Integrating
predictive with prescriptive maintenance, the availability to move beyond just predicting
favorits to prescribing specific recommended actions to prevent them,
based on analytics and historical data.
This course was designed in pen modules. For optimal results,
please follow the sequence. We start with a bit of history, and highlight why predictive
maintenance is such a key aspect for industrial corporations in
the past few years. The gap between what companies are doing, and what is
now possible to do has increases, so there is even more opportunity
to improve. In the module one, we're going to talk about
evolution of maintenance and predictive imperatives. Understanding the historical progression
of maintenance from reactive to proactive approach, and the critical need for predictive
maintenance in today's industrial landscape. We're going to talk about introduction
to the maintenance maturity models, in the role of maintenance, as a strategic material
driver, changing environment, exploiting impact of machine learning, artificial
intelligence, condition-based monitoring, digital tools, and it must be 4.0 on mother maintenance.
What a module too, with the foundation elements,
for effective predictive maintenance. There are some prerequisites for success,
so we're going to emphasize the importance of strong fundamental, such effective
work-order systems, acid documentation, and safety practices. Maintenance
fundamentals includes work-order, equipment history, daily maintenance reports,
and keypies that lay the groundwork for data-driven decisions.
In the module tree, we're going to talk about planet maintenance, and equipment criticality assessment.
The planet maintenance routines, differentiate between planet and reactive maintenance, and understand
the benefits of schedule interventions. CNMS, computerizing
maintenance system, recognize the CNMS as a cornerstone for centralizing maintenance
information. We need it as a single source of information. Equipment
criticality ranking. That's one of the key elements in our course here. Not
all equipment is the same. We need to learn to classify equipment based on its
impacts on operations to prioritize predictive maintenance efforts.
In the module four, we're going to talk about doing introduction to Relability Center
maintenance, RCM and predictive maintenance, PDN. Focus on
reliability, shift from solely preventing breakdowns to optimizing Relability by understanding
Relability Center maintenance, exploring the principles of RCM to determine
appropriate maintenance strategies for different assets and understand how to make preventive
maintenance routines, more effective and efficient, leading to fewer and unnecessary
replacements with placement of spare parts. Module five,
predictive technologies and data acquisition. We're going to talk about condition-based
monitoring CBM, Delve into Core of CBM, and its role in detecting the onset of
degradation, common PTM technologies. It's for various predictive acknowledge
applications, including vibration, ultrasound, thermography, oil analysis, topology, voltage,
root-resistant, insulation integrity, and high-speed cameras.
Module six, we're going to talk about data analysis and interpretation for
predictive insights, analyzing pork patterns, anomalies, and trends. Learn
to interpret data collected from a predictive tools to identify potential
issues before they lead to failures. The agonostic capabilities, understand
how adornance and advancements in sensor-tech norms have enhanced the diagnostic capabilities of predictive
methods. The tail equipment history utilizes historical data to understand
past failures and informed future predictive models.
At the module 7, we're going to go deeper on fault elimination and root cause analysis.
Realability studies in fault elimination, how to effective, use studies like failure mode in effect analysis
and single points of failures to eliminate recurring faults. root cause analysis,
developers' cues and identify the underlying cause of equipment failures based on predictive insights.
And we know of opportunities. Historically, schedule maintenance during periods of minimally
packed, informed by predictive analysis. Module 8,
Adonance predictive and prescriptive methods. Prescriptive methods, or also
knows of methods in 4.0. We're going to explore the most of the method form of proactive methods, which
use AI and machine learning to recommend specific actions to prevent failures. The role
of AI and machine learning understand how AI and now are used to analyze vast datasets
and predict potential issues with greater accuracy. Online monitoring
and automation discuss the benefits of integrating online monitoring from defectory
and automating a maintenance tasks we are feasible. Digital Twins, Introduction to Digital
Twins and their application in simulating equipment behavior in predicting performance.
Module 9, optimizing costs. We're going to talk about total
cost of ownership and understand why tissue is the relevant cost and the
ultimate goal for maintenance and if for the lowest sustainable cost possible. tissue
in practice, how to integrate tissue into decision making such as selecting
better spare parts that last longer to minimize replacement costs and the ROI
of predictive maintenance. I'm going to talk also about the
financial impact, how to focus on tissue enhances financial performance
and transform maintenance from a cost center to a valid driver.
Last, the module time, implementing the end-optimizing and work-class
maintenance program. Strategic implementation, developed by RotMap, from transition
from reactive to a work-class predictive maintenance culture, emphasizing consistency
and discipline. Continuous improvement, apply continuous improvement methodologies
and systematic problem solving to enhance maintenance efficiency. Imagine
success and sustainable excellence, defining key performance indicators,
for predicted maintenance and focus on achieving the lowest sustainable cost and high
efficiency. And people, as a cornerstone, recognize the
crucial role of a high performing team, as well as ongoing training, coaching
and leadership in achieving and sustaining the world-class performance.