# Machine Failure Prediction Using Survival Analysis
## 2 Related Work
The authors plan to address this limitation in future work by exploring the use of an unsupervised fault detector based on a variational autoencoder to optimize performance. Introduced in [20] is a method based on DCNNs to diagnose faults in induction motors using multiple signals. The proposed method leverages the advantages of DCNNs in automatic feature extraction and achieves improved diagnostic performance by combining information from multiple sensor signals. The authors conducted experiments on a dataset containing multiple types of faults in induction motors and evaluated the performance between two different architectures of their proposed method. The first architecture utilized a multichannel model that merged two separate time-frequency images from vibration signals and current signals, forming a two-channel image. This image was then fed into a deep model that consisted of three 2D convolutional layers and a fully connected layer with ReLU activation functions. The output layer had six units that correlated with six distinct labels. The second architecture used two convolutional networks were utilized to analyze different sensor signals separately, and then merged in fully connected layers to contribute to the output of label prediction. One network was trained on vibration signals, while the other was trained on current signals. The learned fault signatures from each network were combined by flattening them into a fully connected layer with 1024 ReLUs. The output layer used for predicting the state label was the same as the one used in architecture 1. The confidence interval analysis showed that the proposed multi-signal DCNN model had stable performance and the merged model outperformed the multi-channel model, with a 95% likelihood of covering fault classification skill between 99.89% and 99.93%. To address the issue of limited training data for deep architectures, the authors suggest the use of data augmentation techniques to expand the dataset and exploring pre-existing models for fault diagnosis as fields of improvement for their future work. The paper [21] proposes a method for equipment failure diagnosis that addresses the challenge of limited data and imbalanced data distribution. Specifically, the proposed method combines the synthetic minority oversampling technique (SMOTE) with a conditional tabular generative adversarial network (CTGAN) to predict equipment failures with a mixture of numerical and categorical data. The experimental results show that the proposed method outperforms other similar methods in five-category failure classification, even when failure data account for less than 1% of the total data. The proposed model showed a high recall rate of 0.9068, an accuracy of 0.8712, and a balanced accuracy of 0.8883. The recall rate and balanced accuracy were the highest across all methods tested by the authors which, apart from the crated model, were a CatBoost (non-oversampling) model, a combination of SmoteNC and CatBoost, and finally, another combination of the ctGAN and CatBoost models. It is noteworthy that the highest accuracy was obtained using the CatBoost algorithm without oversampling. Moreover, the paper highlights the importance of false positives in equipment failure prediction, as the cost of sudden machine downtime far exceeds that of system misdiagnosis. Therefore, the proposed method aims to increase the possibility of false positives to reduce the possibility of false negatives. The authors also note that the interpretability of the equipment failure prediction results is crucial, and they incorporated a tree-based model for failure prediction to analyze the causes of failures and implement preventive measures accordingly. In [22], the authors make a comparative study to evaluate a plethora of machine learning techniques for the task of fault detection and classification. The models used in this study are SVM classifier, KNN classifier, random forest, logistic regression, and decision tree.