To Örebro University

This paper presents a vibration–based, observer–driven fault detection scheme for rotary–percussive drilling that is both physics–grounded and robust to operating–regime variability. A compact axial–torsional plant with single–cutter bit–rock interaction captures loading/unloading asymmetry and frictional torque coupling. To compensate for salient nonlinearities and premise uncertainty (e.g., intermittent contact, rate effects), we embed the dynamics in an interval type–2 Takagi–Sugeno (IT2–T–S) fuzzy framework with explicitly defined IF–THEN rules and type reduction, yielding a convex blend of local linear models suitable for analysis and synthesis. An adaptive Luenberger observer is then designed to (i) reconstruct the nominal vibration response, (ii) generate a residual sensitive to faults yet tolerant to modelling errors and measurement noise, and (iii) deliver an online estimate of an unknown axial fault input. A Lyapunov function with vertex LMI conditions guarantees exponential convergence in the fault–free case and uniform ultimate boundedness under bounded faults; the fault–estimation update law is derived to ensure closed–loop stability. Simulations with percussion–style axial forcing demonstrate three key outcomes on a short time horizon: residuals remain within a noise–based threshold pre–fault and cross the band at the fault onset; the estimated fault rapidly converges to the true magnitude with negligible steady bias; and the state–error norm decays quickly pre–fault and exhibits a bounded transient post–fault. The results indicate that the proposed IT2–T–S adaptive observer provides an implementation–ready path to reliable, vibration–based fault detection for drilling systems. The paper concludes with recommendations to migrate to higher–order fuzzy consequents (polynomial/type–2) to further reduce approximation error and tighten residuals in strongly impacting regimes.

Online weld joint inspection by non-destructive testing is necessary for modern joining industries. Nondestructive testing gained popularity through its dominance in examinations and reliability in confirming the part’s excellence. Joining dissimilar metals is preferable in industries due to reduction in the mass of components and less cost of manufacturing using the safety and structural requirements in various applications ranging from automotive to railway and naval trades. The weld joint imperfection examination plays a significant role in the manufacturing industry. A setup of Gas Tungsten Arc Welding (GTAW) has been proposed for joining stainless steel grades of 316L, 310 and 410 thick sheets of 150 × 60 × 3 mm using variable process parameters. An autonomous technique known as Computer Aided Graphical User Interface (CAGUI) has been proposed for online detection and classification of multiform weld joint imperfections precisely comprising of crack, undercut, gas pores, porosity, tungsten inclusion, wormholes, lack of penetration, and non-defects in radiographic images using Support Vector Machine (SVM) and Recurrent Neural Network (RNN) developed using a MATLAB workbench. The support vector machine classifier has classified the weld images by finding the best hyperplane that separates all the weld joint images into defects and non-defect classes. SVM has classified the weld joint defects and non-defect images and confirmed their accuracy performance as 97.50% using the confusion matrix. It confirmed the lack of penetration defects are erroneous for gas pores. A RNN classifier handles the nonlinear weld joint images along with the parallel processing of information and flexibility in system. The feedforward neural network classified weld joint defects and non-defect and confirmed their accuracy performance as 98.75% using a confusion matrix. The confusion matrix confirmed that the lack of penetration defects is erroneous for undercut. The proposed CAGUI improved the computation period without disturbing the correctness of features selection. 

In this paper we show how relational representations of design structure matrices (DSM), on the one hand, enables to describe domain dependencies and connections as relational composition, and, on the other hand, invites to using a variety of algebraic structures for the sets of qualifications attached with non-binary matrices. Particularly, we use fuzzy sets of higher types to model qualifications in many-valued DSMs where compositional techniques allow for extending the use of fuzzy sets of higher types also in the setting of multidomain matrices (MDM). We further show how clustered domains can be embedded as modelled within powersets of domains, thus providing a further justification for adopting the relational view of DSMs, particularly as the qualification space needs to support folding and unfolding across hierarchies in clustered domains. Our case study is drawn from scenarios involving maintenance of equipment in mineral mining.

Automated inspection in lithium-ion battery manufacturing has emerged as a significant enabler of quality assurance, process optimization, and operational efficiency. Defect detection at several stages from electrode manufacturing to cell packaging has a direct impact on battery safety, performance, and durability. Traditional machine vision approaches were early solutions for detecting electrode flaws, but recent improvements in deep learning, hybrid image processing, and AI assisted inspection have considerably improved accuracy, resilience, and real-time capabilities. This study proposes a comprehensive automated inspection framework that incorporates 3D structured-light profiling, high-speed 2D line-scan imaging, and edge-computing-enabled analytics throughout the production process. The system controls coating uniformity, calendering integrity, geometric precision, electrode alignment, and weld quality, all while ensuring comprehensive digital traceability via MES integration. Vision-guided robotic handling improves assembly accuracy, throughput, and process reliability. By merging multidimensional sensing modalities with AI-driven analysis, the proposed framework assures high-throughput, defect-free battery manufacture while lowering waste, boosting sustainability, and promoting Industry 5.0 digitalization.