Fault slip-rate variations during crustal-scale strain localisation, central Italy

Rates of plate motion are generally uniform over 10–102 Myrs timescales. Faults between tectonic plates might, therefore, be expected to show temporally-uniform slip-rates if the same number of faults remain active. For an extending region of the Eurasia-Africa plate boundary, Italy, finite throw values (vertical component of the slip) for seismogenic normal faults are less than that predicted when recent throw-rates are extrapolated over the fault lifetimes. The effect correlates with distance from the fault system tips and demonstrates that the slip-rates on centrally-located faults have increased with time. Neighbouring normal faults were active in the Quaternary but show no signs of surface faulting during the latest Pleistocene to Holocene. Death of these faults has provided the extra strain per unit time to drive the increased slip-rates measured on other faults. Thus, fault interaction and death modify slip-rates and seismic hazards associated with plate tectonics

A Bank of Reconfigurable LQG Controllers for Linear Systems Subjected to Failures

An approach for controller reconfiguration is presented. The starting point in the analysis is a sufficiently accurate continuous linear time-invariant (LTI) model of the nominal system. Based on a bank of reconfigurable LQG controllers, each designed for a particular combination of total faults, the reconfiguration consists of two operation modes. In the first mode a switching is invoked towards one of the pre-designed LQG controllers on the basis of the information about only the combination of total faults that is in effect. In the second mode, which is activated in cases of partial and component faults, a dynamic correction procedure is initiated which tries to reconfigure the currently active controller in such a way, that the failed closed-loop system remains stable and its performance is as close as possible to the performance of the closed-loop system with only total faults present in the system. In cases of partial faults the second mode is practically an extension of the modified pseudo-inverse method. In cases of component faults the second mode is based on an LMI optimization problem. The approach is illustrated using a model of a real-life space robot manipulator, in which total, partial and component faults are simulate

Influence of near-fault effects and of incident angle of earthquake waves on the seismic inelastic demands of a typical Jack-Up platform

In this paper, the potential influence of near-fault effects and of the incident angle of earthquake waves to the seismic response of a typical jack-up offshore platform is assessed by means of incremental dynamic analysis involving a three dimensional distributed plasticity finite element model. Two horizontal orthogonal strong ground motion components of a judicially chosen near-fault seismic record is considered to represent the input seismic action along different incident angles. The fault-normal component exhibits a prominent forward-directivity velocity pulse pulse-like) whose period lies close to the fundamental natural period of the considered structure following a “worst case scenario” approach, while the fault-parallel component does not include such a pulse. Pertinent numerical data demonstrate that the fault normal component poses much higher seismic demands to the “prototype” jack-up structure considered compared to the fault parallel component. Further, significant variation in the collapse resistance/capacity values is observed among different incident angles especially for the “critical” fault normal component. It is concluded that the combined effect of forward-directivity phenomena and the orientation of deployed jack-up platforms with respect to neighbouring active seismic faults needs to be explicitly accounted for in site-specific seismic risk assessment studies. Further research is warranted to propose recommendations on optimum orientation of jack-up structures operating in the proximity of active seismic faults to minimize seismic risk

Observer-based Fault Detection and Diagnosis for Mechanical Transmission Systems with Sensorless Variable Speed Drives

Observer based approaches are commonly embedded in sensorless variable speed drives for the purpose of speed control. It estimates system variables to produce errors or residual signals in conjunction with corresponding measurements. The residual signals then are relied to tune control parameters to maintain operational performance even if there are considerable disturbances such as noises and component faults. Obviously, this control strategy outcomes robust control performances. However, it may produce adverse consequences to the system when faults progress to high severity. To prevent the occurrences of such consequences, this research proposes the utilisation of residual signals as detection features to raise alerts for incipient faults. Based on a gear transmission system with a sensorless variable speed drive (VSD), observers for speed, flux and torque are developed for examining their residuals under two mechanical faults: tooth breakage with different degrees of severities and shortage of lubricant at different levels are investigated. It shows that power residual signals can be based on to indicate different faults, showing that the observer based approaches are effective for monitoring VSD based mechanical systems. Moreover, it also shows that these two types fault can be separated based on the dynamic components in the voltage signals

A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults

A challenging problem in aircraft engine health management (EHM) system development is to detect and isolate faults in system components (i.e., compressor, turbine), actuators, and sensors. Existing nonlinear EHM methods often deal with component faults, actuator faults, and sensor faults separately, which may potentially lead to incorrect diagnostic decisions and unnecessary maintenance. Therefore, it would be ideal to address sensor faults, actuator faults, and component faults under one unified framework. This paper presents a systematic and unified nonlinear adaptive framework for detecting and isolating sensor faults, actuator faults, and component faults for aircraft engines. The fault detection and isolation (FDI) architecture consists of a parallel bank of nonlinear adaptive estimators. Adaptive thresholds are appropriately designed such that, in the presence of a particular fault, all components of the residual generated by the adaptive estimator corresponding to the actual fault type remain below their thresholds. If the faults are sufficiently different, then at least one component of the residual generated by each remaining adaptive estimator should exceed its threshold. Therefore, based on the specific response of the residuals, sensor faults, actuator faults, and component faults can be isolated. The effectiveness of the approach was evaluated using the NASA C-MAPSS turbofan engine model, and simulation results are presented

Baseline Data from Servo Motors in a Robotic Arm for Autonomous Machine Fault Diagnosis

Fault diagnosis can prolong the life of machines if potential sources of failure are discovered and corrected before they occur. Supervised machine learning, or the use of training data to enable machines to discover these faults on their own, makes failure prevention much easier. The focus of this thesis is to investigate the feasibility of creating datasets of various faults at both the component and system level for a servomotor and a compatible robotic arm, such that this data can be used in machine learning algorithms for fault diagnosis. The faults induced at the component level in different servomotors include: low lubrication, no lubrication, two gears chipped, and four gears chipped. Each fault was also examined at 180, 135, 90, and 45-degree swings of the servo arm. Component level data was obtained using an Arduino microcontroller and a feedback wire in each servomotor to obtain the actual position of the servo arm, which allowed for the calculation of the difference in actual and theoretical position and the speed of the servo arm at the various faults. System level data was obtained using OptiTrack’s motion tracking software, Motive, to track the position of two reflective markers on the hand of the robotic arm. At the component level, the low lubrication and no lubrication faults did not exhibit a large difference from the normal servomotor, whereas the servomotors with the gears chipped exhibited significant differences when compared to the normal servomotor. When evaluating the difference in position and speed of the servo arm at larger degree sweeps it was more evident that failure occurred, as opposed to the data at smaller degree sweeps. At the system level, the error was not as visible in the data as there wasn’t much distinction between the speeds of the robotic arm’s hand when the servomotors with faults were placed in it. The results of this work indicate that servomotors can be used to create fault behavior datasets at the component and system level that are usable for machine learning

Sensor-fault tolerance using robust MPC with set-based state estimation and active fault isolation

In this paper, a sensor fault-tolerant control (FTC) scheme using robust model predictive control (MPC) and set theoretic fault detection and isolation (FDI) is proposed. The MPC controller is used to both robustly control the plant and actively guarantee fault isolation (FI). In this scheme, fault detection (FD) is passive by interval observers, while fault isolation (FI) is active by MPC. The advantage of the proposed approach consists in using MPC to actively decouple the effect of sensor faults on the outputs such that one output component only corresponds to one sensor fault in terms of FI, which can utilize the feature of sensor faults for FI. A numerical example is used to illustrate the effectiveness of the proposed scheme.Postprint (author’s final draft