# A Review on Vibration-Based Condition Monitoring of Rotating Machinery
## 4 Analytical Review
### Category p3: Diagnostics  
In the CM process, after having collected the signals and carried out all the necessary operations on them, including the extraction and selection of the features, the diagnostic phase must be performed, i.e., the recognition of the component or system fault conditions. As emerged in the preliminary analysis of the review works and in the further literature prospective review, the research activity related to the methodologies to carry out the diagnostic phase is very intense, mainly regarding the methods used to realize it automatically, such as machine learning (ML) techniques. Multiple ML techniques have been developed in recent years, in the most varied application contexts. The analysis of the papers classified in the diagnostic category allowed to derive a taxonomy of the main classes of ML methods used for this purpose, as shown in Figure 10. Based on the presence or absence of target values associated with the inputs (i.e., supervised or unsupervised learning) and on the characteristics of the assigned output variables (numerical or categorical), three main classes are identified: (i) supervised learning with numerical output variables, which performs a regression task; (ii) supervised learning with categorical output variables, which performs a classification task; (iii) unsupervised learning, which performs a clustering task [27]. Although in the proposed taxonomy the hybrid cases are not considered, and the method classes reported are not intended to be exhaustive of those present in literature, they represent the most investigated ones in the last two decades for vibration-based diagnostics in rotating machinery. The main interest, for the purposes of diagnostics, is aimed at classification methods, for fault classification. In Table 4 the selected papers for the review, previously inserted in the diagnostic class, are further classified, depending on the considered method. Classical statistical methods allow a regression task, but to be used for automatic diagnostic, threshold values need to be identified, in order to decide when an error magnitude is sufficient to be of concern. This is a major challenge: an inadequate alarm triggering may cause false alarms or, even more critical, do not report an alarm when necessary. Due to this criticality of the threshold-based methods, often classification methods are preferred. As emerges through the analyses and as confirmed in other literature reviews [27], ANNs [208], DNNS, and SVMs are the most investigated methods, and among them DNNs have been dominant in the last years. Figure 10: Taxonomy of the most investigated ML diagnostic methods. [PERSON] and [PERSON], in 2009 [31], reviewed the techniques successfully implemented for the automated fault diagnosis of bearings until that time, and refer to expert systems developed with multilayer perceptron (MLP), radial basis function (RBF) and probabilistic neural network (PNN). More recently, [PERSON] et al., in 2019 [222], adopted a multilayer gated recurrent unit (MGRU) method for gear fault diagnosis; a comparison with long short-term memory (LSTM), multilayer LSTM (MLSTM), and support vector machine (SVM) LSTM, MLSTM, GRU, and SVM models, based on an experimental analysis, revealed improved accuracy with the MGRU network. [PERSON] et al., in 2020 [228], present the positive results obtained with a neuro-fuzzy intelligent diagnostic system in the time and frequency domain, with fuzzy rules of the IF-THEN form. [PERSON] et al. in 2021 [224] present an ANN, with the Levenberg-Marquardt learning algorithm, capable of detecting faults in an induction motor under different operating conditions.