hello everyone and welcome to this webinar series entitled model-based design or parity
maintenance my name is Francesco Larry CEO and I'm an application engineer the MathWorks today
I'd like to take in a very exciting journey with me and the journey we're gonna go on together
is about developing and deploying predictive maintenance algorithms for a flow pack machine a
float back machine as you can see it's basically part of a packaging machine and all it does is
pushing boxes and packets and a conveyor belt so as you might imagine is very important that we are
able to detect if there is any abrupt fault on the electrical side or on the mechanical side and it's
also very important to know how much time we have before this machine fails so their journey we're
gonna go on together it's this one so we're gonna start from using a similar model as a digital twin
to generate synthetic failure data and then we're gonna use this data and we're going to
pre-process it a little bit after pre processing the data we're going to extract what we will call
condition indicators so essentially condition indicators are features so it's basically
useful information that is able to describe and summarize the health condition of our system we
will use such condition indicators to build a couple of models particularly we are going to
build a classification model that we will use to detect and distinguish between electrical folds or
mechanical folds or maybe both and then also we're going to use the condition indicators to build a
remaining useful life estimation model and this model will essentially tell us how much time we
have before with a certain probability the machine fails and then we're going to jump to the last
step which is deployment so you know eventually we want to do we want to bring the algorithms
that we have developed in MATLAB and Simulink onto some real hardware or any way into the
real environment and with respect to these we're going to take a look at a couple of workflows
so the first workflow is deploying the condition monitoring algorithm on to a PLC through automatic
code generation and then the second workflow we're going to look at is deploying the remaining useful
life estimation as a standalone application or as a web app that then we can distribute to people
without a MATLAB license so before we go on let me just briefly summarize what kind of questions
predictive maintenance can answer so the first kind of question is very simple and is you know
basically entering a question like is my machine working well or not and this is what we call
anomaly detection then we can take this a step further and we can look at condition monitoring
so with predictive maintenance algorithms we can essentially understand what is not working
the real system and why and in particular in our case we're going to see how we can build
models that can detect and diagnose failures on the server motor so the electrical side and
the gearbox so the mechanical side and finally prognostics can answer questions like how much
time I have before my machine fails and to do this we're going to build a predictive algorithm and
particularly it's remaining useful life estimation algorithm that will tell us how much time we have
before the bearing system fails so now let's go grab a cup of coffee and let's get started