Chris Bingham

Servo Control of Magnetic Gears

This paper considers the analysis and application of magnetic gearbox and magnetic coupling technologies and issues surrounding their use in high performance servo control systems. An analysis of a prototype magnetic coupling is used as a basis for demonstrating the underlying nonlinear torque transfer characteristics, nonlinear damping, and “pole-slipping” features when subjected to overtorque (overload) conditions. It is also shown how pole-slipping results in a consequential loss of control. A theoretical investigation into the suppression of mechanical torsional resonances in transmission systems encompassing these highly compliant magnetically coupled components is included along with experimental results from a demonstrator drive train. Automatic detection of pole slipping and a reconfigurable controller are also investigated. By addressing these issues, the proposed techniques extend the application scope of magnetic gear/coupling technologies to more demanding applications than those hitherto considered possible-specifically, for use in servo control systems and high-bandwidth mechanical drive trains.

Magnetic Gear Pole-Slip Prevention Using Explicit Model Predictive Control

This paper considers the phenomenon of “pole slipping” in magnetic gears and couplings as a result of torque overload. Specifically, previously reported work on optimized servo speed control and pole-slip detection in magnetic gears is extended through the development of a new control scheme to prevent pole slipping due to combined controller and load torque overload. By utilizing a model predictive control (MPC) strategy, the controllers principal objective is to prevent the magnetic gear from pole slipping by invoking hard constraints on the amount of controller torque that can be applied for a given steady-state load torque. A custom demonstrator drivetrain incorporating a magnetic coupling (1:1 magnetic gear) is used to experimentally verify pole-slip prevention using an implementation of explicit MPC via multiparametric quadratic programming (mp-QP). It is shown that while conventional MPC is restricted to systems with relatively low sample rates, due to the need to solve the constrained optimization problem in real time, an alternative explicit form of MPC can be readily utilized at sample rates more typically found in electrical drive applications. The underlying principles and benefits afforded by the proposed techniques are validated using simulation and experimental measurements from the drivetrain test facility, using only motor-side sensor measurements and load-side state estimates via a discrete-time observer.

Joint Empirical Mode Decomposition and Sparse Binary Programming for Underlying Trend Extraction

This paper presents a novel methodology for extracting the underlying trends of signals via a joint empirical mode decomposition (EMD) and sparse binary programming approach. The EMD is applied to the signals and the corresponding intrinsic mode functions (IMFs) are obtained. The underlying trends of the signals are obtained by the sums of the IMFs where these IMFs are either selected or discarded. The total number of the selected IMFs is minimized subject to a specification on the maximum absolute differences between the denoised signals (signals obtained by discarding the first IMFs) and the underlying trends. Since the total number of the selected IMFs is minimized, the obtained solutions are sparse and only few IMFs are selected. The selected IMFs correspond to the components of the underlying trend of the signals. On the other hand, the L∞ norm specification guarantees that the maximum absolute differences between the underlying trends and the denoised signals are bounded by an acceptable level. This forces the underlying trends to follow the global changes of the signals. As the IMFs are either selected or discarded, the coefficients are either zero or one. This problem is actually a sparse binary programming problem with an L0 norm objective function subject to an L∞ norm constraint. Nevertheless, the problem is nonconvex, nonsmooth, and NP hard. It requires an exhaustive search for solving the problem. However, the required computational effort is too heavy to be implemented practically. To address these difficulties, we approximate the L0 norm objective function by the L1 norm objective function, and the solution of the sparse binary programming problem is obtained by applying the zero and one quantization to the solution of the corresponding continuous-valued L1 norm optimization problem. Since the isometry condition is satisfied and the number of the IMFs is small for most of practical signals, this approximation is valid and verified via our experiments conducted on practical data. As the L1 norm optimization problem can be reformulated as a linear programming problem and many efficient algorithms such as simplex or interior point methods can be applied for solving the linear programming problem, our proposed method can be implemented in real time. Also, unlike previously reported techniques that require precursor models or parameter specifications, our proposed adaptive method does not make any assumption on the characteristics of the original signals. Hence, it can be applied to extract the underlying trends of more general signals. The results show that our proposed method outperforms existing EMD, classical lowpass filtering and the wavelet methods in terms of the efficacy.

Energy harvesting and power network architectures for the multibody advanced airship for transport high altitude cruiser–feeder airship concept

This article presents results of preliminary investigations in the development of a new class of airship. Specific focus is given to photo-electric harvesting as a primary energy source, power architectures and energy audits for life support, propulsion and ancillary loads to support the continuous daily operation of the primary airship (cruiser) at stratospheric altitudes (∼15u2009km). The results are being used to drive the requirements of the FP7 multibody advanced airship for transport programme, which is to globally transport both passengers and freight using a ‘feeder–cruiser’ concept. It is shown that there is a potential trade off to traditional cost and size limits and, although potentially very complex, a first-order approximation is used to demonstrate sensitivities to the economics of the lifting gas. This presented concept is substantially different to those of conventional aircraft due to the airship size and the inherent requirement to harvest and store sufficient energy during ‘daylight’ operation to guarantee safe operation during ‘dark hours’. This is particularly apparent when the sizing of the proposed electrolyser is considered, as its size and mass increases nonlinearly with decreasing daylight duty. The study also considers the integration of photovoltaics with various electrical architectures, in safety critical environments. A mass audit is also included that shows that if the electrolyser was omitted in such systems, the overall impact will be small compared to structural and propulsion masses. It should be noted that although the technology bias is application specific, the underlying principles are much widely applicable to other energy harvesting and power management sectors.

Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models

This paper extends traditional Gaussian mixture model (GMM) techniques to provide recognition of operational states and detection of emerging faults for industrial systems. A variational Bayesian method allows a GMM to cluster with its mixture components to facilitate the extraction of steady-state operational behaviour; this is recognised as being a primary factor in reducing the susceptibility of alternative prognostic/diagnostic techniques, which would initiate false-alarms resulting from control set-point and load changes. Furthermore, a GMM with an outlier component is discussed and applied for direct novelty/fault detection. An advantage of the variational Bayesian method over traditional predefined thresholds is the extraction of steady-state data during both full- and part-load cases, and a primary advantage of the GMM with an outlier component is its applicability for novelty detection when there is a lack of prior knowledge of fault patterns. Results obtained from the real-time measurements on the operational industrial gas turbines have shown that the proposed technique provides integrated preprocessing, benchmarking, and novelty/fault detection methodology.

Development and Realization of Changepoint Analysis for the Detection of Emerging Faults on Industrial Systems

An online two-dimensional changepoint detection algorithm for sensor-based fault detection is proposed. The methodology consists of a differential detector, which looks for characteristics across datasets at a particular instant, and a standard detector, which when combined can identify anomalies and meaningful changepoints while maintaining low rates of false-alarm generation. A key aspect of changepoint detection methodologies is the setting of relevant thresholds, which are typically based on empirical trial and error. Here, a statistical methodology is adopted, which provides the engineer with a tradeoff between correct detection and false-alarm rates, thereby informing decision making at the design stage. The efficacy of the techniques is demonstrated through application to two industry case studies of fault detection on industrial gas turbines, and are shown to readily provide an early warning indicator of impending failures.

A new adaptive Mamdani-type fuzzy modeling strategy for industrial gas turbines

The paper presents a new system identification methodology for industrial systems. Using the original Mamdani fuzzy rule based system (FRBS), an adaptive Mamdani fuzzy modeling (AMFM) is introduced in this paper. It differs from the original Mamdani FRBS in that it applies different membership functions and a denazification mechanism that is `differentiable with respect to the membership function parameters. The proposed system also includes a back error propagation (BEP) algorithm that is used to refine the fuzzy model. The efficacy of the proposed AMFM approach is demonstrated through the experimental trails from a compressor in an industrial gas turbine system.

Machine fault detection during transient operation using measurement denoising

The paper reports and demonstrates a computationally efficient method for machine fault detection in industrial turbine systems. Empirical mode decomposition (EMD) and Savitzky-Golay smoothing filters are used for signal denoising, with a resulting noise index being developed. By comparing the noise index with a power index (also derived in the paper), obtained from the detection of transients using a spectral analysis of the rate-of-change of unit power, three operational conditions are identifiable viz. normal operation, transient operation and operation when subject to emerging machine faults. The accommodation of transient operational conditions of the unit, so as not to create excessive `false alerts, provides a valuable alternative to more traditional techniques, based on PCA for instance, that can only provide reliable information during steady-state operation. The efficacy of the proposed approaches is demonstrated through the use of experimental trials on sub-15MW gas turbines.

Novelty detection based on extensions of GMMs for industrial gas turbines

The paper applies the application of Gaussian mixture models (GMMs) for operational pattern discrimination and novelty/fault detection for an industrial gas turbine (IGT). Variational Bayesian GMM (VBGMM) is used to automatically cluster operational data into steady-state and transient responses, where extraction of steady-state data is an important preprocessing scenario for fault detection. Important features are extracted from steady-state data, which are then fingerprinted to show any anomalies of patterns which may be due to machine faults. Field data measurements from vibration sensors are used to show that the extensions of GMMs provide a useful tool for machine condition monitoring, fault detection and diagnostics in the field. Through the use of experimental trials on IGTs, it is shown that GMM is particularly useful for the detection of emerging faults especially where there is a lack of knowledge of machine fault patterns.

Steady-state and transient operation discrimination by Variational Bayesian Gaussian Mixture Models

The paper presents a Variational Bayesian (VB) method to allow a Gaussian Mixture Model (GMM) to be clustered automatically with its mixture components in order to facilitate the discrimination of what can be regarded as steady-state and transient machine operation. The determination of whether a unit is considered to be in steady-state, or subject to external transients is an important pre-processing scenario for both sensor- and machine-fault detection algorithms, for instance, Principal Component Analysis (PCA) based Squared Prediction Error (SPE), which is known to produce excessive `false alarms when fed with measurements that include transient unit operation. Here, the resulting Variational Bayesian Gaussian Mixture Model (VBGMM) method is utilized to discriminate the operational behaviour of industrial gas turbine systems. Daily batches of measurement data from in-the-field systems are used to show that the VBGMM provides a useful pre-processing tool for subsequent diagnostic and prognostic algorithms.