# A Vibration Based Automatic Fault Detection Scheme for Drilling Process Using Type-2 Fuzzy Logic
## 1 Introduction
created models for predicting the machined surfaces roughness in a complex correlation between tool wear, machining time, and cutting power utilizing AI, keeping the intention of combining AI algorithms with online monitoring of automated manufacturing [23]. In the work by [PERSON] et al., the importance of sensor data is highlighted and also stated that computer assisted electronic and mechanical systems with tool condition monitoring technology clears the path for machining industry and the prospect and development of Industry 4.0 [24]. [PERSON] et al. proposed an innovative data-driven system in order to detect faults and diagnose status variable identification (SVID) data in semiconductor manufacturing.The experimental outcomes reveal that the suggested data driven system can provide quality fault detection performances and supply important information associated with the critical SVIDs and conjugate processing time for fault diagnostics [25]. For early failure identification of the CNC machine tool under time-varying settings, a unique data mining method was proposed by [PERSON] et al. [26]. A technique of reasoning and computation known as fuzzy logic uses classes with ambiguous (fuzzy) boundaries as its objects. Everything, including degrees, is allowed to be, or is, a matter of degree in fuzzy logic. Fuzzy logic is primarily employed in its broad sense today. In particular, fuzzy logic in its broad sense is what is used in practically all applications of fuzzy logic [27]. For more than 40 years, type-1 fuzzy sets have served as the cornerstone of an effective method for simulating uncertainty, ambiguity, and imprecision [28]. A compelling case is made for the use of fuzzy logic to influence perceptions by [PERSON] [29]. His claim is that fuzzy logic is better appropriate for modelling perceptions than standard mathematical methods since perceptions of size, safety, health, and comfort, for example, cannot be modelled using these methods. The debate about perception modelling is fresh and fascinating. It is evident that type-2 fuzzy sets, which have membership functions that are not crisp, can model these perceptions better than type-1 fuzzy sets, whose membership grades are crisp [30]. A type-2 fuzzy set smears out the point value of the membership grade of a type-1 fuzzy set to model uncertainty about that value by extending [PERSON]'s original fuzzy set, now commonly referred to as a Type-1 Fuzzy Set, from a fuzzy set whose membership grade is a single (point) value to a fuzzy set whose membership grade is a function [31]. In the real world, most of the physical systems involve nonlinearities. The T-S fuzzy model has the capability to provide effective design technique associated with nonlinear systems utilizing effective control methodologies as well as techniques in linear systems [32]. Exhaustive investigation reveals that the T-S fuzzy concept has been applied vastly for fault detection problems [33]. An extra degree of freedom (DOF) known as the uncertainty footprint is included in the type-2 fuzzy logic system. This is why the type-2 fuzzy technique demonstrates more effective performance when compared to the type-1 fuzzy technique. system [34; 35]. The concept and methodology of type-2 fuzzy logic system was stated elaborately by [PERSON] et al. [36]. [PERSON] et al. [37] demonstrated that the type-2 fuzzy logic system can handle uncertainties in a better way than type-1 fuzzy logic systems due its characteristics of having more parameters as well as more design DOF. [PERSON] et al. [38] conducted research that validates that a type-2 fuzzy technique has superior capabilities and is implemented in the application associated with a robotic arm. In the work by [PERSON] et al. [39], a new method of defuzzification for type-2 fuzzy intervals utilizing the Aumann integral was proposed.