## 1 Introduction
Spectral analysis transforms the signal from the time domain to the frequency domain using a fast Fourier transform. However, spectral analysis applies only to stationary signals. Waveform analysis helps to provide fundamental information about fault locations. However, this analysis requires complex algorithms for detecting faults [15]. Envelop analysis detects impacts with very low energy often hidden by other vibration signals [16]; however, it is very difficult to identify the small variations in the signal. All these shortfalls can be overcome by adopting a statistical feature analysis. Statistical analysis is the process of collecting and analyzing data to identify patterns and trends. [PERSON] et al. reported a novel statistical feature analysis for facial recognition applications [17]. [PERSON] et al. studied tool insert conditions using statistical features extracted from vibration signals [18]. [PERSON] et al. used statistical feature extraction for developing an iris recognition system [19]. [PERSON] et al. used statistical features for the diagnosis of a rolling element bearing [20]. From the literature, it is evident that statistical features help to gain deeper insights about our data for fault identification. Hence, we propose using statistical analysis to diagnose damage in fiber-reinforced laminated composite structures. A set of statistical features can be extracted from the time-domain vibration response for both laminated and delaminated structures. All the extracted information is not necessarily required for accurate classification. Hence, the selection of highly contributing features is necessitated. Features are generally selected using principal component analysis (PCA), decision tree (DT), attributes evaluator, etc. [PERSON] et al. suggested using CA for selecting the features [21]. PCA is highly sensitive and not appropriate for data sets that have incomplete information [22]. A decision tree (DT), which is a graphical tree-basedstructure, can be used for selecting contributing features. [PERSON] et al. reported that a decision tree was effectively used for feature selection in bearing fault diagnosis [23]. However, the order of features may affect classification accuracy. Hence, we need to identify the order of contributing features. This can be achieved through the attribute evaluator, which eliminates non-contributing features [24]. Feature classification is the most important step in machine learning (ML). Many researchers have applied various ML algorithms to classify the features for diagnosing faults in many applications. The Bayesian family (naive Bayes and Bayes net) and random forest methods have been studied by researchers due to their computational effort and complexity level. [PERSON] et al. made a comparative study of different classification algorithms and found that the decision tree method was an effective choice for fault diagnosis study [25]. [PERSON] et al. used an improved decision tree for diagnosing faults in a VRF system with 98% classification accuracy [26]. [PERSON] and [PERSON] obtained 97% classification accuracy in brake fault diagnosis using a decision tree algorithm [27]. Compared with other algorithms, the decision tree requires less effort for data preparation during pre-processing and does not require normalization of data. Naive Bayes is a multi-class classifier that is used for making predictions in real-time. [PERSON] et al. used a naive Bayes classifier for detecting faults in a transmission line [28]. [PERSON] et al. obtained 98.8% classification accuracy with a naive Bayes classifier model for gear fault diagnosis [29]. [PERSON] et al. achieved 100% accuracy with both the naive Bayes and Bayes net classifier algorithms in bearing fault diagnoses [30]. It is easy and fast to predict the class of the test data set with the Bayesian algorithm. It also performs well in multi-class predictions. [PERSON] et al. used a random forest algorithm for multispectral image classification [31]. [PERSON] et al.