Middeck Active Control Experiment (MACE), phase A

A rationale to determine which structural experiments are sufficient to verify the design of structures employing Controlled Structures Technology was derived. A survey of proposed NASA missions was undertaken to identify candidate test articles for use in the Middeck Active Control Experiment (MACE). The survey revealed that potential test articles could be classified into one of three roles: development, demonstration, and qualification, depending on the maturity of the technology and the mission the structure must fulfill. A set of criteria was derived that allowed determination of which role a potential test article must fulfill. A review of the capabilities and limitations of the STS middeck was conducted. A reference design for the MACE test article was presented. Computing requirements for running typical closed-loop controllers was determined, and various computer configurations were studied. The various components required to manufacture the structure were identified. A management plan was established for the remainder of the program experiment development, flight and ground systems development, and integration to the carrier. Procedures for configuration control, fiscal control, and safety, reliabilty, and quality assurance were developed

Feedforward and Modal Control for a Multi Degree of Freedom High Precision Machine

High precision industrial machines suffer the presence of vibrations due to several noise sources: ground vibration, acoustic noise, direct force disturbances. In the last years the need of higher processing quality and throughput result in a continuing demand for higher accuracy. Therefore vibration isolation systems became mandatory to satisfy these requests. In general, machine supports are designed for high stiffness to obtain a robust machine alignment with respect to its surroundings. However, in the presence of significant ground vibration levels the support stiffness is commonly sacrificed to reduce their transmission to the payload stage. Efforts to go towards these issues are recorded in several applications and the solutions are different for any particular situation, depending on the nature of the vibration sources, the amount of the disturbances and the machine environment. This chapter focuses on the evaluation of a vibration isolation device on the working cell of a micro-mechanical laser center, using active electromagnetic actuators. The machine is composed by two main parts: a frame support and a payload stage where the laser cutting is performed. The machine potential in terms of accuracy and precision is reduced by the presence of two main vibration sources: the ground and the stage itself. The active device should meet two main goals: the payload vibrations damping and the reduction of the transmissibility of ground disturbances. In this work the phases followed to design, realize and validate the device are illustrated with a particular attention to the mechatronics aspects of the project and to the control strategies. The chapter starts on the description of the common solutions and of the techniques described in literature. The requirements analysis and a trade-off phase on the available opportunities for vibration isolation are described. An analysis of the plant components is reported in the second section along with an exhaustive explanation of a) actuation subsystem consisting in four voice-coils, two per axis; b) sensing subsystem aimed to measure the absolute velocities of the frame support and of the stage are measured by means of eight geophone sensors. The considerations leading to the choice of this sensing system are reported along with the signal conditioning block. The active control is performed with a digital platform based on DSP and FPGA. The core of the chapter is the description of the modeling approach and of the control strategies design. The bond-graph approach is used to represent the system behavior, in particular the interactions between the mechanical and electrical subsystems are illustrated. The realized model includes the plant, the sensing, the control and the actuation blocks. The plant is considered as a classical two mass-spring-damper system resulting on a multi-input multi-output system (MIMO), considering disturbances from the stage and the ground and the actuators action between the two masses. Time and frequency domain computations are carried out from the model to evaluate vibration levels and displacements and to identify which parameters need to be carefully designed to satisfy the requirements. The control strategy is focused on the attenuation of the effects of microvibrations on the stage caused by different sources. The technique consists in a combination of two actions, the goal being the minimization of the ground vibrations transmission and the payload vibrations damping: • A single-axis decentralized action consisting in a modal controller used to compensate the high-pass band dynamic of the geophone sensors and to control the vibrations. • A feedforward action working on the disturbances coming from the payload and from the ground. This control is not generated in on-line, but computed in advance from the data of machine responses to the direct disturbances coming from the floor and the stage and resulting in vibrations on the payload and on the frame. The first action itself is aimed to perform active isolation and vibration that nevertheless could be not sufficient for severe specifications applications. The feedforward action is hence used to face this shortcoming by suppressing direct disturbance. The controller design phases along with its performance evaluation are described. The chapter concludes on the illustration of the results obtained with the proposed modeling and control strategy

Design of an intelligent embedded system for condition monitoring of an industrial robot

PhD ThesisIndustrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. There are significant implications for operator safety in the event of a robot malfunction or failure, and an unforeseen robot stoppage, due to different reasons, has the potential to cause an interruption in the entire production line, resulting in economic and production losses. Condition monitoring (CM) is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reduce maintenance costs. So, the main focus of this research is to design and develop an online, intelligent CM system based on wireless embedded technology to detect and diagnose the most common faults in the transmission systems (gears and bearings) of the industrial robot joints using vibration signal analysis. To this end an old, but operational, PUMA 560 robot was utilized to synthesize a number of different transmission faults in one of the joints (3 - elbow), such as backlash between the gear pair, gear tooth and bearing faults. A two-stage condition monitoring algorithm is proposed for robot health assessment, incorporating fault detection and fault diagnosis. Signal processing techniques play a significant role in building any condition monitoring system, in order to determine fault-symptom relationships, and detect abnormalities in robot health. Fault detection stage is based on time-domain signal analysis and a statistical control chart (SCC) technique. For accurate fault diagnosis in the second stage, a novel implementation of a time-frequency signal analysis technique based on the discrete wavelet transform (DWT) is adopted. In this technique, vibration signals are decomposed into eight levels of wavelet coefficients and statistical features, such as standard deviation, kurtosis and skewness, are obtained at each level and analysed to extract the most salient feature related to faults; the artificial neural network (ANN) is then used for fault classification. A data acquisition system based on National Instruments (NI) software and hardware was initially developed for preliminary robot vibration analysis and feature extraction. The transmission faults induced in the robot can change the captured vibration spectra, and the robot’s natural frequencies were established using experimental modal analysis, and also the fundamental fault frequencies for the gear transmission and bearings were obtained and utilized for preliminary robot condition monitoring. In addition to simulation of different levels of backlash fault, gear tooth and bearing faults which have not been previously investigated in industrial robots, with several levels of ii severity, were successfully simulated and detected in the robot’s joint transmission. The vibration features extracted, which are related to the robot healthy state and different fault types, using the data acquisition system were subsequently used in building the SCC and ANN, which were trained using part of the measured data set that represents the robot operating range. Another set of data, not used within the training stage, was then utilized for validation. The results indicate the successful detection and diagnosis of faults using the key extracted parameters. A wireless embedded system based on the ZigBee communication protocol was designed for the application of the proposed CM algorithm in real-time, using an Arduino DUE as the core of the wireless sensor unit attached on the robot arm. A Texas Instruments digital signal processor (TMS320C6713 DSK board) was used as the base station of the wireless system on which the robot’s fault diagnosis algorithm is run. To implement the two stages of the proposed CM algorithm on the designed embedded system, software based on the C programming language has been developed. To demonstrate the reliability of the designed wireless CM system, experimental validations were performed, and high reliability was shown in the detection and diagnosis of several seeded faults in the robot. Optimistically, the established wireless embedded system could be envisaged for fault detection and diagnostics on any type of rotating machine, with the monitoring system realized using vibration signal analysis. Furthermore, with some modifications to the system’s hardware and software, different CM techniques such as acoustic emission (AE) analysis or motor current signature analysis (MCSA), can be applied.Iraqi government, represented by the Ministry of Higher Education and Scientific Research, the Iraqi Cultural Attaché in London, and the University of Technology in Baghda

The application of time encoded signals to automated machine condition classification using neural networks

This thesis considers the classification of physical states in a simplified gearbox using acoustical data and simple time domain signal shape characterisation techniques allied to a basic feedforward multi-layer perceptron neural network. A novel extension to the signal coding scheme (TES), involving the application of energy based shape descriptors, was developed. This sought specifically to improve the techniques suitability to the identification of mechanical states and was evaluated against the more traditional minima based TES descriptors. The application of learning based identification techniques offers potential advantages over more traditional programmed techniques both in terms of greater noise immunity and in the reduced requirement for highly skilled operators. The practical advantages accrued by using these networks are studied together with some of the problems associated in their use within safety critical monitoring systems.Practical trials were used as a means of developing the TES conversion mechanism and were used to evaluate the requirements of the neural networks being used to classify the data. These assessed the effects upon performance of the acquisition and digital signal processing phases as well as the subsequent training requirements of networks used for accurate condition classification. Both random data selection and more operator intensive performance based selection processes were evaluated for training. Some rudimentary studies were performed on the internal architectural configuration of the neural networks in order to quantify its influence on the classification process, specifically its effect upon fault resolution enhancement.The techniques have proved to be successful in separating several unique physical states without the necessity for complex state definitions to be identified in advance. Both the computational demands and the practical constraints arising from the use of these techniques fall within the bounds of a realisable system

Experimental study on active structural acoustic control of rotating machinery using rotating piezo-based inertial actuators

info:eu-repo/semantics/publishe