A Vibration Based Automatic Fault Detection Scheme for Drilling Process Using Type-2 Fuzzy Logic

Abstract

The fault detection system using automated concepts is a crucial aspect of the industrial process. The automated system can contribute efficiently in minimizing equipment downtime therefore improving the production process cost. This paper highlights a novel model based fault detection (FD) approach combined with an interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy system for fault detection in the drilling process. The system uncertainty is considered prevailing during the process, and type-2 fuzzy methodology is utilized to deal with these uncertainties in an effective way. Two theorems are developed; Theorem 1, which proves the stability of the fuzzy modeling, and Theorem 2, which establishes the fault detector algorithm stability. A Lyapunov stabilty analysis is implemented for validating the stability criterion for Theorem 1 and Theorem 2. In order to validate the effective implementation of the complex theoretical approach, a numerical analysis is carried out at the end. The proposed methodology can be implemented in real time to detect faults in the drilling tool maintaining the stability of the proposed fault detection estimator. This is critical for increasing the productivity and quality of the machining process, and it also helps improve the surface finish of the work piece satisfying the customer needs and expectations.

Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis

“…When an industrial process presents deviations in its parameters, its outputs generally do not correspond to normal values within the operating range [ 1 , 7 , 8 ]. These deviations could be caused by damage or malfunction of the devices involved in said process.…”

“…These methods take into account only perfect breakdowns for each residual generator. That means the remaining signal-to-noise ratio isn’t measured by diagnostic equipment [ 1 ]. The defect affects all residuals in the figure, but only a subset of them deviates considerably from their expected behavior.…”

“…Residual selection aims to identify generators that can detect and isolate faults. To discover these sets, researchers have created tools like the Binary Integer Linear Programming (BILP) algorithm and the greedy search technique [ 1 , 2 ]. The fault detection capabilities of each potential residual generator are considered equivalent in these pieces.…”

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

“…However, they may suffer from increased computational complexity and a lack of interpretability [38]. Fuzzy Logic Systems offer a framework for handling uncertainty but may struggle with capturing complex nonlinear relationships in data [39]. In contrast, AdaBoost with weak classifiers achieves high predictive accuracy while maintaining computational efficiency [40].…”

Generator of Fuzzy Implications

“…Akisue et al [31] developed a fuzzy dissolved oxygen controller, taking into account a decision tree algorithm presented in the literature, and implemented in the supervision software SUPERSYS_HCDC. The algorithm was coded in MATLAB with its membership function parameters being determined using an Adaptive Network-Based Fuzzy Inference System tool.…”