 Hello, everyone. Welcome and thanks for joining my brief overview of Industry 4.0. I'm Tom Bocchino with the Memson Sensor Product Marketing Team. We're working for Edo here in SD America's region. And I hope everybody's having a great day so far. Okay, so smart industry, let's talk about smart industry. Industry 4.0 and a key trait of smart industry is the capability to sense and exchange data between various parts of the application. I'll call it application system is what I'm calling the application. And to make decisions either locally or in the cloud. But what is driving the IoT is the miniaturization and mass production of low cost sensors plus the availability of low cost microcontrollers and a myriad of connectivity options for many different communications methods. A ton of new applications have emerged actually under the umbrella of smart industry and these applications involve assets and part of my talk is going to be about predictive maintenance. So this is really relative. The assets include large machines, rotating engines, HVAC systems, elevators, escalators, just to name a few. The common thread among these applications is the drive towards efficiency plus environmental awareness. Let's talk about industry 4.0 and specifically machine maintenance, especially in light of the previous applications I talked about. A historical approach to machine maintenance depicted in the top row here is to schedule a periodic maintenance. While this is simple to plan, it often results in downtime when the machine is taken offline plus it's less efficient because we're often replacing perfectly good parts for the sake of prevention. A second option, condition based maintenance calls for the inspection and monitoring of certain parameters and while this is better it can still be more wasteful of critical human resources and it doesn't guarantee that the asset will remain online because it's not at possibly at critical times. With the availability of affordable sensor nodes, things that we're talking about today, the industry 4.0 concept comes to life through predictive maintenance. We can now instrument every asset with sensors and communications in order to profile normal behavior and detect or predict early failures in the machine life cycle. This allows us to properly schedule maintenance resulting in a maximum availability for the asset. Also early detection often results in expensive repairs and a longer life expectancy of the asset itself. Predictive maintenance is implemented as a closed loop system between the equipment for example on the factory floor and the analysis tools which could be in the factory floor in the gateway or in the cloud. The data is collected on the end node and by utilizing distributed communication capabilities the processing of data as I mentioned is accomplished at any of the three places. Where the data is processed is really dependent on the overall goals and capabilities of the customer or company that's deploying the system and there are advantages and disadvantages to each of the methods and where to process the data but overall there is a trend to push more and more decision making to the edge or to the sensor node for reasons of security less data transmission and lower overall power consumption. This is called the installation point of failure curve. By sensing a machine's condition over time we can predict where the machine is in its life cycle. For example after installation of a motor the first symptom of failure is likely to be a change in the ultrasound signature. Being able to monitor in that spec frequency spectrum allows us to spot a trend very early even months before the failure. The next symptom could be vibration and after a period of time other symptoms begin to manifest themselves in predictable way with well understood frequency ranges. They manifest themselves as audible noise, wobble, vibration, heat and eventually smoke but smoke is catastrophic damage. So how can ST technology help? Well for each of these conditions we have sensors low power tiny sensors which can detect even the smallest of signals. These sensors include accelerometers microphones temp sensors just among a few and in addition to sensors we offer turnkey evaluation and development toolkits which including firmware and Excel for example which is very much part of our presentation today. So let's talk about sensors some of the ST sensors into the different failure mode frequencies that I discussed on the previous point of failure curve slide. Here I've mapped the various sensors with their respective frequencies along characteristics along the X axis. For example unbalances and low frequency content and you can you can identify this using an accelerometer such as or an IMU such as ISM 330 DHCX which has a gyro in case of non-stationary equipment and those are for frequencies typically below 2.5 kilohertz or in that range. Bearings and cavitation failure modes would require the sensor to detect vibrations at higher frequencies and here we cover it with our latest vibration sensor IS3 with up to five kilohertz signal bandwidth and at higher frequencies standard and ultrasound microphones would be used to capture signals with frequencies at five kilohertz and much higher even up to 80 and 100 kilohertz. One point is that each of these sensors that I've mentioned here are included in our industrial product lineup which come with guaranteed 10 years of longevity. About the sensors and let's talk about predictive maintenance and the sensors so let's talk about the tools. All of these sensors output signals in the time domain given us a real-time signature in order to plot the behavior over time the signals must be converted to the frequency domain using a fast Fourier transform or FFT. The results can then be transmitted in a smaller size any of the communications methods that we mentioned with little or no impact on bandwidth. One of the tools that we offer for predictive maintenance is STWIN pictured here. This is an easy to use eval kit which includes a broad combination of sensors embedded BLE for data streaming plus an optional Wi-Fi module for cloud enablement. It has an onboard STM32 L4 or pletary and a charger for remote installations. It's truly with cloud connectivity and pre-made software examples that I'll share in a moment. The sensors on STWIN include motion sensors, acoustic sensors, environmental sensors really we've placed an abundance of sensors on this platform including multiple accelerometers, multiple temp sensors, a few microphones so you can capture signals across the frequency spectrum very easily with all the sensors. With STWIN you can capture the data from one or all of the sensors and process it locally or in the cloud it's really a great tool for or as a baseline for a new product development. Our goal is to make your design cycle as turnkey as possible and this reflects on the survey that we did previously so significantly in terms of firmware and resources firmware resources and software example. On top of the STWIN hardware kit we offer free of charge function pack FP IND predictive maintenance one which is a collection of application examples running on the STWIN hardware. The function pack can be downloaded from the STWIN web page under software and resources and in addition we've created a very easy to use cloud dashboard that Manuel mentioned previously. The cloud I've pictured it here the cloud dashboard can be easily configured by customers to enroll their STWIN, publish results, analyze the data and monitor assets in your feasibility and evaluation stages. Finally to wrap up my presentation in addition to STWIN ST offers a really comprehensive end-to-end offering of ecosystem enablement for IoT. In addition to our chips we have modular evaluation and development boards many of which we're talking about today many software examples and SDKs and we've enabled many partner companies partners as well. In addition ST has an extensive set of software partners for both hardware and software development. So that's it for my presentation I'd like to pass a sincere thank you to everybody for listening and taking a few minutes of your valuable day.