Development of a Condition Monitoring Algorithm for Industrial Robots based on Artificial Intelligence and Signal Processing Techniques

Signal processing plays a significant role in building any condition monitoring system. Many types of signals can be used for condition monitoring of machines, such as vibration signals, as in this research; and processing these signals in an appropriate way is crucial in extracting the most salient features related to different fault types. A number of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. This chapter starts with a discussion of the proposed robot condition monitoring algorithm. Then, a consideration of the signal processing techniques which can be applied in condition monitoring is carried out to identify their advantages and disadvantages, from which the time-domain and discrete wavelet transform signal analysis are selected

Fault Diagnosis of Industrial Robot Bearings Based on Discrete Wavelet Transform and Artificial Neural Network

Industrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. 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. The majority of the previous research on industrial robots health monitoring is focused on monitoring of a limited number of faults, such as backlash in gears, but does not diagnose the other gear and bearing faults. Thus, the main aim of this research is to develop an intelligent condition monitoring system to diagnose the most common faults that could be progressed in the bearings of industrial robot joints, such as inner/outer race bearing faults, using vibration signal analysis. For accurate fault diagnosis, time-frequency signal analysis based on the discrete wavelet transform (DWT) is adopted to extract the most salient features related to faults, and the artificial neural network (ANN) is used for faults classification. A data acquisition system based on National Instruments (NI) software and hardware was developed for robot vibration analysis and feature extraction. An experimental investigation was accomplished using the PUMA 560 robot. Firstly, vibration signals are captured from the robot when it is moving one joint cyclically. Then, by utilising the wavelet transform, signals are decomposed into multi-band frequency levels starting from higher to lower frequencies. For each of these levels the standard deviation feature is computed and used to design, train and test the proposed neural network. The developed system has showed high reliability in diagnosing several seeded faults in the robot

Self-repair during continuous motion with modular robots

Through the use of multiple modules with the ability to reconfigure to form different morphologies, modular robots provide a potential method to develop more adaptable and resilient robots. Robots operating in challenging and hard-to-reach environments such as infrastructure inspection, post-disaster search-and-rescue under rubble and planetary surface exploration, could benefit from the capabilities modularity offers, especially the inherent fault tolerance which reconfigurability can provide. With self-reconfigurable modular robots self-repair, removing failed modules from a larger structure to replace them with operating modules, allows the functionality of the multi-robot organism as a whole to be recovered when modules are damaged. Previous self-repair work has, for the duration of self-repair procedures, paused group tasks in which the multi-robot organism was engaged, this thesis investigates Self-repair during continuous motion, ``Dynamic Self-repair", as a way to allow repair and group tasks to proceed concurrently. In this thesis a new modular robotic platform, Omni-Pi-tent, with capabilities for Dynamic Self-repair is developed. This platform provides a unique combination of genderless docking, omnidirectional locomotion, 3D reconfiguration possibilities and onboard sensing and autonomy. The platform is used in a series of simulated experiments to compare the performance of newly developed dynamic strategies for self-repair and self-assembly to adaptations of previous work, and in hardware demonstrations to explore their practical feasibility. Novel data structures for defining modular robotic structures, and the algorithms to process them for self-repair, are explained. It is concluded that self-repair during continuous motion can allow modular robots to complete tasks faster, and more effectively, than self-repair strategies which require collective tasks to be halted. The hardware and strategies developed in this thesis should provide valuable lessons for bringing modular robots closer to real-world applications

Conformal Additive Manufacturing and Cooperative Robotic Repair and Diagnosis

In the past several years exponential growth has occurred in many industries, including additive manufacturing (AM) and robotics, enabling fascinating new technologies and capabilities. As these technologies mature, the need for higher-level abilities becomes more apparent. For instance, even with current, commercial state-of-the-art technology in AM it is impossible to deposit material onto a nonplanar surface. This limitation prevents the ability to fully encase objects for packaging, to create objects with hollow features or voids, and even to retrofit or repair preexisting items. These limitations can be addressed by the introduction of a conformal AM (CAM) process or more concretely the process in which material is deposited normal to the surface of an object as opposed to solely planar layers. Therefore, one of the main contributions of this work is the development of two novel methods to generate layers from an initial object to a desired object for use in two- and three-dimensional CAM processes. The first method is based on variable offset curves and subject to mild convexity conditions for both the initial and desired object. The second method reparametrizes solutions to Laplace's equation and does not suffer from these limitations. A third method is then presented that alters solutions from the previous methods to incorporate hollow features or voids into the layer generation process. Although these hollow features must obey mild convexity conditions, the location and number of said features is not limited. Examples of all three layering methods are provided in both two- and three-dimensions. Interestingly, these same methods can also be applied to determine the collision-free configuration space in certain robot motion planning applications. However, ultimately, the most compelling application may be in the repair of damaged items. Given an accurate model of a damaged item, these techniques, in conjunction with fused deposition modeling devices embedded on robotic arms, can be leveraged to restore a damaged item to its original condition. In a separate but similar vein, although robotic systems are becoming more capable each day, their designs still lack almost any semblance of a repair mechanism. This issue is increasingly important in situations where robotic systems are deployed to isolated or even hostile environments as human intervention is limited or impossible. The second half of this work focuses on solving this issue by introducing the Hexagonal Distributed Modular Robot (HexDMR) System which is capable of autonomous team repair and diagnosis. In particular, agents of the HexDMR system are composed of heterogeneous modules with different capabilities that may be replaced when damaged. The remainder of this work discusses the design of each of these modules in detail. Additionally, all possible non-isomorphic functional representations of a single agent are enumerated and a case study is provided to compare the performance between two possible iterations. Then, the repair procedures for an agent in the system are outlined and verified through experiments. Finally, a two-step diagnosis procedure based on both qualitative and quantitative measures is introduced. The particle filter based quantitative portion of this procedure is verified through simulation for two separate robot configurations, while the entire procedure is validated through experiments

Characterisation of a nuclear cave environment utilising an autonomous swarm of heterogeneous robots

As nuclear facilities come to the end of their operational lifetime, safe decommissioning becomes a more prevalent issue. In many such facilities there exist ‘nuclear caves’. These caves constitute areas that may have been entered infrequently, or even not at all, since the construction of the facility. Due to this, the topography and nature of the contents of these nuclear caves may be unknown in a number of critical aspects, such as the location of dangerous substances or significant physical blockages to movement around the cave. In order to aid safe decommissioning, autonomous robotic systems capable of characterising nuclear cave environments are desired. The research put forward in this thesis seeks to answer the question: is it possible to utilise a heterogeneous swarm of autonomous robots for the remote characterisation of a nuclear cave environment? This is achieved through examination of the three key components comprising a heterogeneous swarm: sensing, locomotion and control. It will be shown that a heterogeneous swarm is not only capable of performing this task, it is preferable to a homogeneous swarm. This is due to the increased sensory and locomotive capabilities, coupled with more efficient explorational prowess when compared to a homogeneous swarm

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

Autonomous Operation of a Reconfigurable Multi-Robot System for Planetary Space Missions

Reconfigurable robots can physically merge and form new types of composite systems. This ability leads to additional degrees of freedom for robot operations especially when dynamically composed robotic systems offer capabilities that none of the individual systems have. Research in the area of reconfigurable multi-robot systems has mainly been focused on swarm-based robots and thereby to systems with a high degree of modularity but a heavily restricted set of capabilities. In contrast, this thesis deals with heterogeneous robot teams comprising individually capable robots which are also modular and reconfigurable. In particular, the autonomous application of such reconfigurable multi-robot systems to enhance robotic space exploration missions is investigated. Exploiting the flexibility of a reconfigurable multi-robot system requires an appropriate system model and reasoner. Hence, this thesis introduces a special organisation model. This model accounts for the key characteristics of reconfigurable robots which are constrained by the availability and compatibility of hardware interfaces. A newly introduced mapping function between resource structures and functional properties permits to characterise dynamically created agent compositions. Since a combinatorial challenge lies in the identification of feasible and functionally suitable agents, this thesis further suggests bounding strategies to reason efficiently with composite robotic systems. This thesis proposes a mission planning algorithm which permits to exploit the flexibility of reconfigurable multi-robot systems. The implemented planner builds upon the developed organisation model so that multi-robot missions can be specified by high-level functionality constraints which are resolved to suitable combinations of robots. Furthermore, the planner synchronises robot activities over time and characterises plans according to three objectives: efficacy, efficiency and safety. The plannera s evaluation demonstrates an optimization of an exemplary space mission. This research is based on the parallel development of theoretical concepts and practical solutions while working with three reconfigurable multi-robot teams. The operation of a reconfigurable robotic team comes with practical constraints. Therefore, this thesis composes and evaluates an operational infrastructure which can serve as reference implementation. The identification and combination of applicable state-of-the-art technologies result in a distributed and dynamically extensible communication infrastructure which can maintain the properties of reconfigurable multi-robot systems. Field tests covering semi-autonomous and autonomous operation have been performed to characterise multi-robot missions and validate the autonomous control approach for reconfigurable multi-robot systems. The practical evaluation identified critical constraints and design elements for a successful application of reconfigurable multi-robot systems. Furthermore, the experiments point to improvements for the organisation model. This thesis is a wholistic approach to automate reconfigurable multi-robot systems. It identifies theoretical as well as practical challenges and it suggests effective solutions which permit an exploitation of an increased level of flexibility in future robotics missions