Predictive Maintenance With Sound
A proof-of-concept that uses an Arduino to listen for anomolies in the sound of a running motor.
Every manufacturing environment is equipped with machines. For a better-performing manufacturing unit, the health of machines plays a major role and hence maintenance does. We have three strategies of maintenance namely - Preventive maintenance, Corrective maintenance, and Predictive maintenance.
If you want to find the best balance between preventing failures and avoiding over-maintenance, PdM is the way to go. Equip your factory with relatively affordable sensors to track temperature, vibrations, and motion data, use predictive techniques to schedule maintenance when a failure is about to occur, and you'll see a nice reduction in operating costs.
In the newest era of technology, teaching computers to make sense of the acoustic world is now a hot research topic. So in this project, we use sound to do some predictive maintenance with Nano 33 BLE Sense.
We use Nano 33 BLE Sense to listen to the machine continuously. The MCU runs an ML model which is trained on two sets of acoustic anomalies and a normal operation mode. When the ML model identifies an anomaly, the operator is immediately notified and the machine may be shut down for maintenance after proper inspection. Thus we can reduce the possible damage caused and can reduce the downtime.
- Nano 33 BLE Sense
- LED
- Edge Impulse
- Arduino IDE
The hardware setup consists of a Nano 33 BLE Sense, which is placed beside an old AC motor.
Motor
Nano 33 BLE Sense
If you haven't connected the device to Edge Impulse dashboard, follow this tutorial to get it connected. After a successful connection, it should be present in the Devices tab.
Devices
Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your development board, without the need for the Edge Impulse CLI. Follow this tutorial to learn more about it.
After successfully setting up hardware and software, now we are ready for the model generation which includes a number of steps. Let's start by collecting some data.
Clean data is the most important requirement to train a well-performing model. In our case, we have collected 3 classes of sound - two classes of anomalies, one normal operation class, and obviously a noise class. Each sample is 2 seconds long. The raw data of these classes is visualised below.
OK
Anomaly 1
Anomaly 2
Noise
If the data is not split into training and testing datasets, split the dataset into training and testing datasets in the ratio 80:20, which forms a good dataset for model training.
An impulse is the machine learning pipeline that takes raw data, uses signal processing to extract features, and then uses a learning block to classify new data.
Impulse Architecture
Here we are using the Time Series data as the input block. Now, we have two choices for the processing block - MFCC and MFE. As we are dealing with non-vocal audio and MFE performs well with non-vocal audio, we have chosen MFE as our processing block. We have used Classification as our learning block since we have to learn patterns and apply them to new data to categorize the audio into one of the given 4 classes.
MFE
In the MFE tab, you can tweak the parameters if you're good with audio handling, else leave the settings as it is and generate features.
Features
Now that we have our impulse designed, let's proceed to train the model. The settings we employed for model training are depicted in the picture. You can play about with the model training settings so that the trained model exhibits a higher level of accuracy, but be cautious of overfitting.
Training Settings
A whopping 94.7% accuracy is achieved by the trained model.
Training Accuracy
Let's now use some unknown data to test the model's functionality. To assess the model's performance, move on to Model Testing and Classify All.
Testing Accuracy
We have got 95.07% testing accuracy, which is pretty awesome. Now let's test the model with some real-world data. Navigate to Live Classification and collect some data from the connected device.
OK
Anomaly 1
Anomaly 2
We have collected some real-world data of Normal Operation Mode, Anomaly 1, and Anomaly 2 respectively, and all of them are correctly classified. So our model is ready for deployment.
For deployment, navigate to the Deployment tab, select Arduino Library and build the library. It will output a zip library, which can be added to Arduino IDE.
Deployment
Nano 33 BLE Sense along with an LED is enclosed in a 3D printed case, which is our final product. The device is capable of identifying acoustic anomalies in a machine and alerts the user using the alert LED.
Case.JPG
Device.JPG