Machine-Predictive-Maintainence-Anomaly-Detection

Anomaly Detection deployed on machine data dataset for Predictive Maintenance .

NOTE : I have also included the code files for predictive maintainence using XGBoost , using LSTMs and using Autoencoders (seq2seq and CNN based ) which I learnt from Nvidia Deep learning course , in this repository . However , these were only included as a reference to readers on what other methods could be used and for the sake of completeness , and are not written by me . The code for multivariate gaussian method is written completely by me :)

• Name - Ayush Agarwal
• Project - Predictive Maintainence Anomaly Detection
• Skills - Anomaly Detection, Multivariate Gaussian , Unsupervised Learning , Data Visualisation , Exploratory Data Analysis , Data Science , Machine Learning
• Tools - Google Colab , Jupyter Notebooks , Python , Numpy , Pandas , Matplotlib , Seaborn , Sklearn

Real Life Application :

This can be deployed in an Industrial Internet of Things (IIoT) System , where the system variables measured throught the sensors are sent to Processing System (ofc with my algorithm inside it ) and can be used to monitor system health and predict need for maintainence in the Mechatronic System . Since Prevention is better than cure , a maintainence before any damage or major inconvenience happens is better , hence our system can reduce maintainence costs thus being a business boon .

The Dataset :

The data was taken from Kaggle site : https://www.kaggle.com/datasets/shivamb/machine-predictive-maintenance-classification .

A copy of the dataset has been uploaded on this repository just in case the kaggle one gets changed : https://github.com/ayush-agarwal-0502/Machine-Predictive-Maintainence-Anomaly-Detection/blob/main/predictive_maintenance.csv .

The Code :

The code is availaible at : https://github.com/ayush-agarwal-0502/Machine-Predictive-Maintainence-Anomaly-Detection/blob/main/anomaly_detector_ML_project.ipynb (in this repository itself ) .

Aim :

To build an Anomaly Detection system for Predictive Maintainence work on Machines data using Multivariate Gaussian fitting and thresholding , a simple unsupervised learning technique .

Data Preprocessing and Visualisation :

Removing UID , ProductID since they are not useful in prediction . Removing Failure type as here our aim is just to predict failure or not , rather than the type of failure itself .

Using Ordinal Encoding for Type , as it is categorical data .

Scaling the dataset for better fitting of the gaussian .

Fitting the Gaussian :

Making predictions , deciding threshold :

Result :

The Confusion Matrix was satisfactory , and the F1 score of my model was in range of that of competitors (https://www.kaggle.com/datasets/shivamb/machine-predictive-maintenance-classification/code) who have used advanced techniques such as XGBoost , hence showing that the project was successful . Furthermore , the threshold can be shifted to decrease the number of false negatives . The data is as such that the anomalies and the non anomalies are a bit mixed , and hence we can't expect extreme high accuracies . F-Beta Score can also be considered as a better marking factor here if we wanted to emphasise over false negatives over the cost of false positives .