# A Vibration Based Automatic Fault Detection Scheme for Drilling Process Using Type-2 Fuzzy Logic
## 1 Introduction  
Superior progress in the field of production process involves implementation of the automatic fault detection scheme as it contributes significantly in an industrial sector by lowering devices downtime as well as maintenance costs. Fault detection and notification to the operator in real time associated with a complex system is the main intention of automated fault detection system [1]. For the assurance of safety and superior performances of nonlinear complex systems, the methodology of fault detection has been considered to be very popular among the scientific researcher and is applied widely particularly concentrating on the approaches of state observers [2, 3]. The fault detection technique can subdivided into [4, 5, 6, 7]:  
1. signal-based method;
2. data-driven method;
3. model-based method;  
The analysis of spectrum components associated with the measured signals is the main concept behind the signal-based fault detection methodology. The knowledge-based methodology incorporates intelligent techniques like neural networks for the detection of faults. In the model-based methodology, there is a requirement of an exact model of the system in order to simulate the process actual behaviour [8]. The various approachesassociated with model-based fault diagnosis methodology are observer embedded techniques, parity space techniques and the methodology of parameter estimation [9; 10; 11]. The availability of the mathematical model associated with practical system is very crucial in the model-based methodology. This is achievable by implementing a system identification technique or by implementing the physical principles aspects. A model-based approach is implemented effectively in the development of fault detection mechanism [12]. An innovative methodology on the basis of model based technique is applied for the detection of fault in mobile robots [13]. [PERSON] et al. [14] proposed a novel observer relied fault detection technique associated with electric vehicles. For dealing with the faults in the nonlinear systems, [PERSON] et al. [15] illustrated a Type-1 Takagi-Sugeno (T-S) fuzzy logic system based observer. In the current era involving advanced manufacturing technology, there is a suitable growth in automated and unattended machining techniques. Thus, there is a requirement of suitable online condition monitoring technology for the minimization of error and wastage of work material. These mechanical failures are the root cause of 79.6% machining tool downtime in modern industries [16]. Hence, it is very important to detect the fault early for the improvement of the product quality and to to cut short the machining downtime [17]. During the machining operation, the factors like cutting tools, various workpieces, several types of cutting parameters, etc. affect the working conditions [18]. Ref. [19] proposed a virtual sensor for the online detection of the fault in multi-toothed tools on the basis of Bayesian classifier. In the work by [PERSON] et al. [20], a blended mechanism involving Support Vector Machine (SVM), Artificial Neural Network (ANN), and Bayes classifier is presented for the detection and classification of faults. In any manufacturing process, the machine generates vibration, which results in degradation of machine tools, thus inducing failures of some subsystems or the machine itself. The analysis of the vibrations signatures can be implemented for the detection of the nature and extent of incorporated damage in machines. A detailed review on devices utilized for vibration computation as well as signal processing methodologies in order to monitor conditions of machine tools associated with manufacturing operations was illustrated in the work by [PERSON] et al. [21]. A detailed analysis on the methodology of tool condition monitoring associated with drilling, turning, milling, and grinding was presented by [PERSON] et al. [22]. [PERSON] et al.