Steven Grainger

Rolling element bearing fault diagnosis using Laplace-wavelet envelope power spectrum

The bearing characteristic frequencies (BCF) contain very little energy, and are usually overwhelmed by noise and higher levels of macro-structural vibrations. They are difficult to find in their frequency spectra when using the common technique of fast fourier transforms (FFT). Therefore, Envelope Detection (ED) has always been used with FFT to identify faults occurring at the BCF. However, the computation of the ED is suffering to strictly define the resonance frequency band. In this paper, an alternative approach based on the Laplace-wavelet enveloped power spectrum is proposed. The Laplace-Wavelet shape parameters are optimized based on Kurtosis maximization criteria. The results for simulated as well as real bearing vibration signal show the effectiveness of the proposed method to extract the bearing fault characteristic frequencies from the resonant frequency band.

Stiffness Analysis and Control of a Stewart Platform-Based Manipulator With Decoupled Sensor–Actuator Locations for Ultrahigh Accuracy Positioning Under Large External Loads

Robot frame compliance has a large negative effect on the global accuracy of the system when large external forces/torques are exerted. This phenomenon is particularly problematic in applications where the robot is required to achieve ultrahigh (micron level) accuracy under very large external loads, e.g., in biomechanical testing and high precision machining. To ensure the positioning accuracy of the robot in these applications, the authors proposed a novel Stewart platform-based manipulator with decoupled sensor–actuator locations. The unique mechanism has the sensor locations fully decoupled from the actuator locations for the purpose of passively compensating for the load frame compliance, as a result improving the effective stiffness of the manipulator in six degrees of freedom (6DOF). In this paper, the stiffness of the proposed manipulator is quantified via a simplified method, which combines both an analytical model (robot kinematics error model) and a numerical model [finite element analysis (FEA) model] in the analysis. This method can be used to design systems with specific stiffness requirements. In the control aspect, the noncollocated positions of the sensors and actuators lead to a suboptimal control structure, which is addressed in the paper using a simple Jacobian-based decoupling method under both kinematics- and dynamics-based control. Simulation results demonstrate that the proposed manipulator configuration has an effective stiffness that is increased by a factor of greater than 15 compared to a general design. Experimental results show that the Jacobian-based decoupling method effectively increases the dynamic tracking performance of the manipulator by 25% on average over a conventional method.

Intelligent modeling of a piezoelectric tube actuator

Various model-based control methods are currently used in control of piezoelectric tubes, others such as internal model control and model predictive control are anticipated to be employed soon. All these control systems are designed based on black box models. However, systematic black box modeling of piezoelectric tubes has been overlooked in the literature to a large extent or has been presented in a too brief and faulty way. In this article, a novel structure of artificial neural networks is used to model and to assess the nonlinearity of piezoelectric actuators. Apart from nonlinearity, other features of the achieved models like delay time, sampling time, orders as well as system identification process are clearly stated, and more importantly, it is clarified that different definitions of accuracy are needed for different purposes of black box modeling, with change in model features, the accuracy may decrease for one purpose (e.g. predictive control) and increase for another one (e.g. simulation). This highly critical point has never been raised and addressed in modeling of piezoelectric tubes, and a definition of accuracy which suits static systems/models has been widely used in the past to assess models of piezoelectric tubes which are obviously dynamic. Experimental results support the proposed modeling ideas.

Implementation and analysis of an innovative digital charge amplifier for hysteresis reduction in piezoelectric stack actuators

Smart actuators are the key components in a variety of nanopositioning applications, such as scanning probe microscopes and atomic force microscopes. Piezoelectric actuators are the most common smart actuators due to their high resolution, low power consumption, and wide operating frequency but they suffer hysteresis which affects linearity. In this paper, an innovative digital charge amplifier is presented to reduce hysteresis in piezoelectric stack actuators. Compared to traditional analog charge drives, experimental results show that the piezoelectric stack actuator driven by the digital charge amplifier has less hysteresis. It is also shown that the voltage drop of the digital charge amplifier is significantly less than the voltage drop of conventional analog charge amplifiers.

IMC-based feedforward control of a piezoelectric tube actuator

This article addresses sensorless control of a piezoelectric tube actuator to avoid the expense and practical limits of displacement sensors in nanopositioning. Three electrical signals have been used to estimate displacement: the piezoelectric voltage, the induced voltage and the sensing voltage. In this work, the piezoelectric voltage was employed to estimate displacement which does not require drift removal like the sensing voltage and does not suffer from a time lag respect to displacement like the induced voltage. This signal is the actuating signal at the same time, so the sensorless control system is feedforward. It was shown the relationship between the piezoelectric actuator and displacement is nearly linear at the designated operation area: excitation of the tube by triangular voltage functions with the magnitude up to 60V and the frequency up to 60Hz. Therefore, Internal Model Control (IMC) was employed to design this feedforward controller based on a second order linear discrete model which maps the piezoelectric voltage into displacement. The performance of the proposed feedforward controller has been compared with a well-tuned feedforward P-action controller and a remarkable improvement has been observed.

A digital charge amplifier for hysteresis elimination in piezoelectric actuators

Piezoelectric actuators are commonly used for nanopositioning due to their high resolution, low power consumption and wide operating frequency, but they suffer hysteresis, which affects linearity. In this paper, a novel digital charge amplifier is presented. Results show that hysteresis is reduced by 91% compared with a voltage amplifier, but over long operational periods the digital charge amplifier approach suffers displacement drift. A non-linear ARX model with long-term accuracy is used with a data fusion algorithm to remove the drift. Experimental results are presented.

Properties of neuronal facilitation that improve target tracking in natural pursuit simulations

Although flying insects have limited visual acuity (approx. 1°) and relatively small brains, many species pursue tiny targets against cluttered backgrounds with high success. Our previous computational model, inspired by electrophysiological recordings from insect ‘small target motion detector’ (STMD) neurons, did not account for several key properties described from the biological system. These include the recent observations of response ‘facilitation’ (a slow build-up of response to targets that move on long, continuous trajectories) and ‘selective attention’, a competitive mechanism that selects one target from alternatives. Here, we present an elaborated STMD-inspired model, implemented in a closed loop target-tracking system that uses an active saccadic gaze fixation strategy inspired by insect pursuit. We test this system against heavily cluttered natural scenes. Inclusion of facilitation not only substantially improves success for even short-duration pursuits, but it also enhances the ability to ‘attend’ to one target in the presence of distracters. Our model predicts optimal facilitation parameters that are static in space and dynamic in time, changing with respect to the amount of background clutter and the intended purpose of the pursuit. Our results provide insights into insect neurophysiology and show the potential of this algorithm for implementation in artificial visual systems and robotic applications.

Physics-based modelling of a piezoelectric actuator using genetic algorithm

A number of models have been presented to estimate the displacement of piezoelectric actuators; these models remove the need for accurate displacement sensors used in nanopositioning. Physics based models, inspired by physical phenomena, are widely used for this purpose due to their accuracy and comparatively low number of parameters. The common issue of these models is the lack of a non-ad-hoc and reliable method to estimate their parameters. Parameter identification of a widely accepted physics-based model, introduced by Voigt, is addressed in this paper. Non-linear governing equation of this model consists of five parameters needing to be identified. This research aims at developing/adopting an optimal and standard (non-ad-hoc) parameter identification algorithm to accurately determine the parameters of the model and, in a more general view, all physics-based models of piezoelectric actuators. In this paper, Genetic Algorithm (GA) which is a global optimisation method is employed to identify the model parameters.

A system identification approach to the characterization and control of a piezoelectric tube actuator

This paper addresses the sensorless control of piezoelectric tube actuators to avoid the expense and practical limits of displacement sensors in nanopositioning applications. Three electrical signals have traditionally been used to estimate displacement: the piezoelectric voltage, the voltage induced in sensing electrodes and the voltage across a sensing resistor. In this work, the piezoelectric voltage was employed to estimate displacement; the use of this signal does not necessitate drift removal like the sensing voltage, and its superiority over the induced voltage is shown in this paper. The piezoelectric voltage is the actuating signal, so a feedforward architecture based on an inverse model is used for sensorless control. Inspired by internal model control (IMC), a filter together with the inverted model of the system, derived using system identification techniques, was used as the feedforward controller. The fixed-slope-input effect is illustrated as a prominent source of control error in tracking triangular references, then an additional nonlinear control command is proposed to address this effect and improve the control performance.

Spike-timing-dependent construction

Spike-timing-dependent construction (STDC) is the production of new spiking neurons and connections in a simulated neural network in response to neuron activity. Following the discovery of spike-timing-dependent plasticity (STDP), significant effort has gone into the modeling and simulation of adaptation in spiking neural networks (SNNs). Limitations in computational power imposed by network topology, however, constrain learning capabilities through connection weight modification alone. Constructive algorithms produce new neurons and connections, allowing automatic structural responses for applications of unknown complexity and nonstationary solutions. A conceptual analogy is developed and extended to theoretical conditions for modeling synaptic plasticity as network construction. Generalizing past constructive algorithms, we propose a framework for the design of novel constructive SNNs and demonstrate its application in the development of simulations for the validation of developed theory. Potential directions of future research and applications of STDC for biological modeling and machine learning are also discussed.