Paper review

Reviewer1: Major revision

A machine learning-based workflow for automatic detection of anomalies in machine tools is discussed in your paper. This topic is interesting and has certain practical significance. However, there are also some problems need to be considered:

1) The features and advantages of your proposed anomalies detection method should be described briefly in the Abstract.

2) The 'Industry 4.0' and 'Clustering' in the Keywords are not mentioned in the Abstract.

3) All the references cited in your Introduction dates back to at least 5 years ago, and the number of cited references is small, from this, I don't think it is sufficient of your Introduction to indicate the up-to-date research status about your field. Thus, I cannot find obvious innovations in your study. If there are any important novel points in your paper compared with nowadays existing research results, please highlight it and make brief description.

4) Please pay attention to your English grammar carefully. The methods in any existing reference were proposed at some time in the past, so the tense in your introduction should be modified. Furthermore, the overuse of first person in your paper should be paid attention.

5) It is better to insert the figures and tables into the corresponding location of your manuscript for faster correspondence with the content.

6) The description of your proposed approach should include some necessary equations, such as the clustering process, the mapping process and some other calculating processes. Besides, some confusing concepts need to be clarified, for example, weather the timestamp label and the cluster label refer to same labels?

7) Please check the citation format of your references carefully and make modifications to meet the requirements of this journal.

Reviewer 2: Rejected

In this paper, an approach that integrates domain knowledge about manufacturing systems into a machine learning-based workflow to identify the current production mode of a multi-purpose production machine as well as its degradation state is proposed. The paper lacks of innovation and the description of the method is unclear.

(1) In the abstract, please pay more attention to the motivation of the paper.

(2) The procedure of the method is unclear. Formulas should be provided.

(3) The machine learning methods used in this paper are traditional machine learning methods, which lacks of innovation.

(4) The references should be improved.