## 1 Introduction  
Mechanical vessels play a very important role in day-to-day life with widespread applications [1]. Specifically, in the oil and gas industry, the use of spherical tanks is required due to the cost effectiveness of building a sphere. With the increasing use of these types of spherical tanks for different industries, the number of accidents related to leakage from the bottoms of these tanks is also increasing [2]. As a result, improved safety precautions and maintenance are required [3, 4]. Moreover, the finding of the crack in its initial stages facilitates required actions to be commenced in a timely manner and reduces the incidence of industrial accidents. Therefore, identifying the health condition (normal or faulty state) through signals at an early stage will make it easier to establish the necessary precautions for condition monitoring purposes. The present crack detection procedures for spherical tanks comprise of observing inbound signals. However, there is still a lack of automated multiclass identification methods for achieving this objective. In this study, the central focus is to categorize the health states of a spherical tank, assessed through acoustic emission (AE) signals. Compared with traditional methods, AE is a cost-effective and useful for fault detection applications [5]. Additionally, AE signals can provide underlying evidence for signals with low energy and is suitable to establish a more substantial data-driven fault identification approach. Conventional data-driven health state classification procedures count on two vital techniques: (1) feature extraction by applying domain proficiency and (2) identifying those health states by utilizing those extracted sets of features. Among different approaches, the popular AE-based diagnosis methodsmostly rely on analyzing the peak of characteristic frequencies in the frequency spectrums of signals [6]. Nevertheless, to obtain intrinsic information from the signals, the selection of this signal analysis procedure has a significant effect on the final performance [6]. In recent studies, several effective feature extraction methods with conventional feature selection algorithms have been proposed. [PERSON] et al. [7] analyzed the statistical characteristics from time, and frequency domain to classify between normal and faulty health conditions of the tank by using support vector machine (SVM). Similarly, [PERSON] et al. [8] developed a heterogeneous time-frequency feature fusion with a genetic algorithm (GA) based feature selector to identify the most distinctive feature information from the data. However, these methods are made for binary class-classification. Moreover, due to sensitive nature of the crack sizes under various faulty conditions, these feature selector algorithms failed to distinguish between multiple crack sizes. Therefore, in this research paper, the main emphasis is on multiclass health state categorization by analyzing the AE signals obtained from a spherical tank. Consequently, pattern generation from acquired signal domains using several signal-imaging techniques can also differentiate between health conditions for further classification [7]. Besides, several automated feature learning processes driven by deep learning-based algorithms have been studied to ease the inevitability of domain knowledge proficiency [9; 10]. However, due to limited amount of data, these deep learning-based approaches are not capable of extracting meaningful features. In this study, a hybrid feature pool-denoted as hybrid bag of features (HBoF) is designed by fusing the statistical properties from AE domain. Additionally, time and frequency domain-based analysis are added to increase the robustness of the proposed feature extractor mechanism. However, to select the most distinctive feature set from the proposed HBoF, it is logical to determine the underlying cause for multiple health conditions.