Machine learning techniques have progressively emerged as important and reliable tools that, when combined with machine condition monitoring, can diagnose faults with even superior performance than other condition-based monitoring approaches. Furthermore, statistical or model-based approaches are often not applicable in industrial environments with a high degree of customization of equipment and machines. Structures such as bolted joints are a key part of the industry; therefore, monitoring their health is critical to maintaining structural integrity. Despite this, there has been little research on the detection of bolt loosening in rotating joints. In this study, vibration-based detection of bolt loosening in a rotating joint of a custom sewer cleaning vehicle transmission was performed using support vector machines (SVM). Different failures were analyzed for various vehicle operating conditions. Several classifiers were trained to evaluate the influence of the number and location of accelerometers used and to determine the best approach between specific models for each operating condition or a single model for all cases. The results showed that using a single SVM model with data from four accelerometers mounted both upstream and downstream of the bolted joint resulted in more reliable fault detection, with an overall accuracy of 92.4%.

No abstract

The problem of crack propagation in viscoelastic materials is of great interest given the numerous engineering applications of such materials. Due to viscoelasticity, even the study of the basic Mode I opening represents a tricky theoretical challenge. Indeed, existing theories adopt important approximations such as i) simplistic constitutive behaviour, ii) steady-state crack propagation, iii) infinite domain of the system. In this work, we revise the theory of Persson & Brener for systems of infinite domain; specifically, we propose a solution to take into account size effects in a viscoelastic plate. The theory allows to consider the realistic constitutive behaviour of viscoelastic materials and to predict the dependence of the energy release rate with the crack tip speed. Comprehensive experimental investigations are performed to corroborate our theoretical predictions. First, dynamic mechanical analysis (DMA) is performed to characterize the complex viscoelastic modulus of PolyTetraFluoroEthylene (PTFE). Second, tensile tests are carried out on cracked PTFE samples, and pictures are recorded with an image acquisition system. Moreover, a point tracking algorithm is developed to measure the crack length and opening displacement. Moving from small to high crack tip speeds, the fracture process becomes less ductile and an increase in the maximum load is observed. In addition, experimental data show that the inclusion of finite-size effects in the theory is crucial for accurately estimating the energy release rate.

No abstract

No abstract