Develop a simple heuristic, such as one based on z-score, to categorize the historical performance data of the machines. Test this heuristic in a live environment to validate its effectiveness.

Employ a team of expert analysts to manually review and categorize the historical performance data of the machines. Use this labeled dataset to build a supervised learning model.

Implement a time-series model to predict the machines' performance metrics. Set up alerts for significant deviations between actual and predicted values, assuming the data's temporal nature is the most critical feature.

Create a simple heuristic, like one based on z-score, to classify the machines' historical performance data. Then, develop a model to detect anomalies based on this classified data, iteratively improving the heuristic and model based on performance insights.

Both A and D are viable first steps, with A providing a quick initial assessment and D offering a more structured approach towards building a predictive model.