Hire a team of data analysts to manually label historical performance data and use this dataset to train a supervised learning model.

Implement a complex deep learning model to directly analyze the unlabeled data and predict failures without any initial labeling.

Apply a simple statistical method, such as calculating z-scores, to automatically label the data as 'normal' or 'anomalous' based on historical performance, then use this labeled data to train a supervised learning model.

Use an unsupervised learning algorithm to cluster the data into 'normal' and 'anomalous' groups without any initial labeling, and then monitor these clusters for changes over time.