*5.2.2 Electrical outline*

Our electrical system was also very straightforward, we simply needed to be able to provide the various currents required by all the system components. In the end we had the following devices actively connected in our system, in order of communication direction **Figure 9**.

#### **Figure 9.**

*Block diagram of the electrical connections for the Vibration Station.*

#### *5.2.3 Software visualization and data flow*

Our first objective for our IIoT vibration station was to get the data flowing and network so that we can visualize the raw information coming from the system. Our goal was to get the data front the sensor to a networked user interface that could be accessed anywhere from the world. This means we need a web app, data acquisition tool, and most importantly a messaging system. For our case we used MQTT, a middleware-based messaging system that allows for pub/sub based information transfer. In the end our data flow looked like follow **Figures 10** and **11**:

#### **Figure 10.**

*Vibration Station data flow components.*

**Figure 11.** *Vibration Station user Interface.*

#### *5.2.4 Data science*

After having accomplished all the other requirements to get data from our system, as well as get that data visualized and networked, our main goal was to create a predictive maintenance model. We used our weighted offset assembly to create 2 main datasets, one where there is no imbalance and second where we add some offset weight and capture the "fault" labeled data. The difference between the FFT output of each can be seen in **Figures 12** and **13**, and as seen there is not a noticeable difference visually in the two plots.

Our first step was a vanilla neural network approach with no preprocessing, we fed the raw current and vibration data into a multi layered (3) neural network and trained it on both the good and bad data with the appropriate labels.

This approach turned out to be flawed, our main thinking in regards to this is that the neural network did not have enough learning capacity to map the given data into the frequency domain, where the faults are clearly indicated. The neural network would first need some sort of temporal mapping, and then on top of that also learn to differentiate between fault and no fault. To combat this issue, we attempted to preprocess the data through an FFT, this would map the data into the frequency domain and THEN through the neural network differentiate the data into it is given label.

This new approach worked much better, with a very high success rate in terms of predicting faults. The training also took only 5 minutes one a laptop, what this means is that the model can be trained on edge and in real time to continuously update itself as the system slowly degrades. Our simple neural network model is given as follows:

**Figure 12.** *FFT plot with No load on the motor.*

**Figure 13.** *FFT plot with imbalance load on the motor.*

#### *5.2.5 Conclusion*

An important takeaway from this project was around the structure of IIoT in a realistic setting as well as a realization of the various benefits IIoT brings, like the ability to visualize and see what is going on in our system in real time, from anywhere. This ability of constant perception into the system combined with a data processing model creates an approach for predictive maintenance that is very powerful, low-cost, and useful in active learning settings.
