This paper presents mathematical modelling of an energy harvester that converts slow mechanical rotation into piezoelectric vibration using a small disk and a pair of magnets, for large-scale machinery monitoring applications such as wind turbine blades. The harvester consists of a piezoelectric cantilevered beam, a gravity-induced disk, and magnets attached to both the beam and the disk. The energy method is used to derive the three coupled equations that describe the motion of the disk, the vibration of the beam, and the harvester voltage output. The equations are solved using ODE45 in MATLAB software and verified by the corresponding experimental study. The result shows varied energy harvesting performance by blade rotational speed. At low blade speed, the harvester generates power by regularized magnetic excitation per blade revolution. At high blade speed, however, the disk behavior becomes chaotic to increase excitation force and power generation. The results show that the model can quantify power as a function of blade speed, and the proposed harvester can generate a considerable amount of power for self-sustainable sensing and monitoring of wind turbine blades.

Energy harvesting (EH) technologies to power small sized electronic devices are attracting great attention. Wasted energy in a vehicle’s rotating tire has a great potential to enable self-powered tire pressure monitoring sensors (TPMS). Piezoelectric type energy harvesters can be used to collect vibrational energy and power such systems. Due to the presence of harsh acceleration in a rotating tire, a design tradeoff needs to be studied to prolong the harvester’s fatigue life as well as to ensure sufficient power generation. However, the design by traditional deterministic design optimization (DDO) does not show reliable performance due to the lack of consideration of various uncertainty factors (e.g., manufacturing tolerances, material properties, and loading conditions). In this study, we address a new EH design formulation that considers the uncertainty in car speed, dimensional tolerances and material properties, and solve this design problem using reliability-based design optimization (RBDO). The RBDO problem is formulated to maximize compactness and minimize weight of a TPMS harvester while satisfying power and durability requirements. A transient analysis has been done to measure the time varying response of EH such as power generation, dynamic strain, and stress. A conservative design formulation is proposed to consider the expected power from varied speed and stress at higher speed. When compared to the DDO, the RBDO results show that the reliability of EH is increased significantly by scarifying the objective function. Finally, experimental test has been conducted to demonstrate the merits of RBDO design over DDO.

Reliability analysis involving high-dimensional, computationally expensive, highly nonlinear performance functions is a notoriously challenging problem in simulation-based design under uncertainty. In this paper, we tackle this problem by proposing a new method, high-dimensional reliability analysis (HDRA), in which a surrogate model is built to approximate a performance function that is high dimensional, computationally expensive, implicit, and unknown to the user. HDRA first employs the adaptive univariate dimension reduction (AUDR) method to construct a global surrogate model by adaptively tracking the important dimensions or regions. Then, the sequential exploration–exploitation with dynamic trade-off (SEEDT) method is utilized to locally refine the surrogate model by identifying additional sample points that are close to the critical region (i.e., the limit-state function (LSF)) with high prediction uncertainty. The HDRA method has three advantages: (i) alleviating the curse of dimensionality and adaptively detecting important dimensions; (ii) capturing the interactive effects among variables on the performance function; and (iii) flexibility in choosing the locations of sample points. The performance of the proposed method is tested through three mathematical examples and a real world problem, the results of which suggest that the method can achieve an accurate and computationally efficient estimation of reliability even when the performance function exhibits high dimensionality, high nonlinearity, and strong interactions among variables.

In recent years, vibration energy harvesters have been widely studied to build self-powered wireless sensor networks for monitoring modern engineered systems. Although there has been significant research effort on different energy harvester configurations, the power output of a vibration energy harvester is known to be sensitive to various sources of uncertainties such as material properties, geometric tolerances, and operating conditions. This article proposes a reliability-based design optimization method to find an optimum design of energy harvester that satisfies the target reliability on power generation. This optimum design of vibration energy harvester demonstrates reliable power generation capability in the presence of the various sources of uncertainties.

Reliability-based design optimization (RBDO) requires the evaluation of probabilistic constraints (or reliability), which can be very time consuming. Therefore, a practical solution for efficient reliability analysis is needed. The response surface method (RSM) and dimension reduction (DR) are two well-known approximation methods that construct the probabilistic limit state functions for reliability analysis. This article proposes a new RSM-based approximation approach, named the adaptive improved response surface method (AIRSM), which uses the moving least-squares method in conjunction with a new weight function. AIRSM is tested with two simplified designs of experiments: saturated design and central composite design. Its performance on reliability analysis is compared with DR in terms of efficiency and accuracy in multiple RBDO test problems.

A robot is essential in many industrial and manufacturing facilities due to its efficiency, accuracy, and durability. However, continuous use of the robotic system can result in various component failures. The servo motor is one of the critical components, and its bearing is one of the vulnerable parts, hence failure analysis is required. Some previous prognostics and health management (PHM) methods are very limited in considering the realistic operating conditions of industrial robots based on various operating speeds, loading conditions, and motions, because they consider constant speed data with unloading conditions. This paper implements a PHM for the servo motor of a robotic arm based on variable operating conditions. Principal component analysis-based dimensionality reduction and correlation analysis-based feature selection are compared. Two machine learning algorithms have been used to detect fault features under various operating conditions. This method is proposed as a robust fault-detection model for industrial robots under various operating conditions. Features from different domains not only improved the generalization of the model’s performance but also improved the computational efficiency of massive data by reducing the total number of features. The results showed more than 90% accuracy under various operating conditions. As a result, the proposed method shows the possibility of robust failure diagnosis under various operating conditions similar to the actual industrial environment.