Arun Kumar Samantaray

Precise remaining useful life (RUL) estimation of components is critical for the prognostic and health management (PHM) of the systems to improve reliability and reduce downtime and maintenance costs. One component may show multiple degradation patterns throughout its life cycle. The degradation trends’ occurrence and recurrence are highly unpredictable. This article suggests an RUL prediction model based on artificial neural network (ANN), for components that show different patterns of degradation while operating under similar working conditions. For the ANN learning, some key time-domain features based on the high correlation of the features with the target output, that is, Life ratio (LR) of the components, are extracted from the history of degradation profiles. Prediction intervals are also estimated to account for the various uncertainties in the degradation profile data. In an application involving accelerated aging of capacitors, when the results of the ANN model are compared to the results of conventional machine learning models for example, Linear Regression, Decision Tree, Support Vector Regression, and Bayesian Neural Network (BNN), it is found that the ANN model gives lowest Mean Square Error (MSE) with limited data, thereby demonstrating the effectiveness of the proposed methodology.

Continuum manipulators are a type of robot used for delicate applications, including safe human-robot interactions. Controlling these manipulators for an accurate trajectory, especially in the case of pneumatic actuation, is extremely challenging. Thus, this article proposes a real-time kinematic trajectory control of a pneumatically actuated multi-segment bionic continuum manipulator with a mobile base by combining a neural network and analytical model with a cascaded controller to overcome this challenge. The inverse kinematics solution of the multi-segment manipulator is developed by using a neural network and an inverse piecewise constant curvature approach. The neural network is trained by using a separate learning algorithm. Although hybrid inverse modeling gives better solutions than existing techniques, significant residual positional error of the manipulator tip remains due to inherent material hysteresis. Thus, a cascaded PI-controller is utilized to compensate for the residual positional error. The controller gains are updated in each step by predictions of the actuator length, where the Jacobian entries are computed from the neural network model. The proposed procedure is validated on Festo Didactics’ elephant trunk-like two-segment continuum manipulator, Robotino-XT. Three different cases are considered for real-time trajectory tracking, where the OptiTrack vision system is used for validation by tracking the manipulator tip pose. For the trajectory points outside the manipulator workspace, simultaneous trunk and base movements are used. In experimental validation, the proposed scheme is shown to give much reduced manipulator tip trajectory error as compared to the existing methods.

In rotating machinery with a flexible foundation used for vibration isolation, the drive (motor) is designed to provide maximum power at the critical speed so that the rotor can be coasted up to a supercritical operating speed. Over the time, rotors develop some degree of unbalance and it may be impossible to balance the rotors in-situ, such as in a wind turbine. Therefore, oversized motors are used in anticipation of some unbalance. To promote passage through resonance, without altering the motor, various kinds of additional hardware and/or control mechanisms have been proposed in the past. On the other hand, simply increasing the foundation (or structural) damping reduces the amplitude of unbalance induced vibrations and the motor power requirement at the critical speed. This appears to be the simplest possible solution and has been proposed in relevant mathematical research. However, it is shown in this article that increasing foundation damping significantly reduces the overall system efficiency at the supercritical operating speed and it should not be at all considered as a meaningful engineering solution. In fact, the loss of efficiency due to the increased foundation damping is so large that it may not be compensated by a redesigned motor.