Consistent HMM parameter estimation using Kerridge inaccuracy rates

In this paper, we propose a novel online hidden Markov model (HMM) parameter estimator based on Kerridge inaccuracy rate (KIR) concepts. Under mild identifiability conditions, we prove that our online KIR-based estimator is strongly consistent. In simulation studies, we illustrate the convergence behaviour of our proposed online KIR-based estimator and provide a counter-example illustrating the local convergence properties of the well known recursive maximum likelihood estimator (arguably the best existing solution).

The velocity synchronous discrete Fourier transform for order tracking in the field of rotating machinery

Diagnostics of rotating machinery has developed significantly in the last decades, and industrial applications are spreading in different sectors. Most applications are characterized by varying velocities of the shaft and in many cases transients are the most critical to monitor. In these variable speed conditions, fault symptoms are clearer in the angular/order domains than in the common time/frequency ones. In the past, this issue was often solved by synchronously sampling data by means of phase locked circuits governing the acquisition; however, thanks to the spread of cheap and powerful microprocessors, this procedure is nowadays rarer; sampling is usually performed at constant time intervals, and the conversion to the order domain is made by means of digital signal processing techniques. In the last decades different algorithms have been proposed for the extraction of an order spectrum from a signal sampled asynchronously with respect to the shaft rotational velocity; many of them (the so called computed order tracking family) use interpolation techniques to resample the signal at constant angular increments, followed by a common discrete Fourier transform to shift from the angular domain to the order domain. A less exploited family of techniques shifts directly from the time domain to the order spectrum, by means of modified Fourier transforms. This paper proposes a new transform, named velocity synchronous discrete Fourier transform, which takes advantage of the instantaneous velocity to improve the quality of its result, reaching performances that can challenge the computed order tracking.

Robust speed tracking control of synchronous motors using immersion and invariance

This paper presents a novel control strategy for velocity tracking of Permanent Magnet Synchronous Machines (PMSM). The model of the machine is considered within the port-Hamiltonian framework and a control is designed using concepts of immersion and invariance (I&I) recently developed in the literature. The proposed controller ensures internal stability and output regulation, and it forces integral action on non-passive outputs.

Sensor fault-resilient control of interior permanent-magnet synchronous motor drives

In a conventional ac motor drive using field-oriented control, a dc-link voltage, speed, and at least two current sensors are required. Hence, in the event of sensor failure, the performance of the drive system can be severely compromised. This paper presents a sensor fault-tolerant control strategy for interior permanent-magnet synchronous motor (IPMSM) drives. Three independent observers are proposed to estimate the speed, dc-link voltage, and currents of the machine. If a sensor fault is detected, the drive system isolates the faulty sensor while retaining the remaining functional ones. The signal is then acquired from the corresponding observer in order to maintain the operation of the drive system. The experimental results provided verify the effectiveness of the proposed approach.

A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter

Interior permanent-magnet synchronous motors (IPMSMs) become attractive candidates in modern hybrid electric vehicles and industrial applications. Usually, to obtain good control performance, the electric drives of this kind of motor require one position, one dc link, and at least two current sensors. Failure of any of these sensors might lead to degraded system performance or even instability. As such, sensor fault resilient control becomes a very important issue in modern drive systems. This paper proposes a novel sensor fault detection and isolation algorithm based on an extended Kalman filter. It is robust to system random noise and efficient in real-time implementation. Moreover, the proposed algorithm is compact and can detect and isolate all the sensor faults for IPMSM drives. Thorough theoretical analysis is provided, and the effectiveness of the proposed approach is proven by extensive experimental results.

Cascade multilevel static synchronous compensator configuration for wind farms

Modulation and control of a cascade multilevel static synchronous compensator (STATCOM) configuration to improve the quality of voltage generated by wind power systems are presented. The proposed STATCOM configuration needs only four dc-link capacitors and 24 switches to synthesise nine-level operation. In addition to that, switching losses are further reduced by splitting the voltage source inverter of the STATCOM into two units called the `bulk inverter` and the `conditioning inverter`. The high-power bulk inverter is operated at low frequency whereas the low-power conditioning inverter is operated at high frequency to suppress harmonics produced by the bulk inverter. Fluctuations at the point of common coupling voltage, caused by sudden wind changes, are suppressed by controlling reactive power of the STATCOM. Simulation and experimental results are presented to verify the efficacy of the proposed modulation and control techniques used in the STATCOM.

Adaptive estimation of HMM transition probabilities

This paper presents new schemes for recursive estimation of the state transition probabilities for hidden Markov models (HMM's) via extended least squares (ELS) and recursive state prediction error (RSPE) methods. Local convergence analysis for the proposed RSPE algorithm is shown using the ordinary differential equation (ODE) approach developed for the more familiar recursive output prediction error (RPE) methods. The presented scheme converges and is relatively well conditioned compared with the ...

On adaptive HMM state estimation

In this paper new online adaptive hidden Markov model (HMM) state estimation schemes are developed, based on extended least squares (ELS) concepts and recursive prediction error (RPE) methods. The best of the new schemes exploit the idempotent nature of Markov chains and work with a least squares prediction error index, using a posterior estimates, more suited to Markov models then traditionally used in identification of linear systems.

Optimal HMM processing of differentially phase modulated communication systems

This paper investigates demodulation of differentially phase modulated signals DPMS using optimal HMM filters. The optimal HMM filter presented in the paper is computationally of order N3 per time instant, where N is the number of message symbols. Previously, optimal HMM filters have been of computational order N4 per time instant. Also, suboptimal HMM filters have be proposed of computation order N2 per time instant. The approach presented in this paper uses two coupled HMM filters and exploits knowledge of ...

Optimal HMM filtering and decision feedback equalisation for differential encoded transmission systems

In this paper conditional hidden Markov model (HMM) filters and conditional Kalman filters (KF) are coupled together to improve demodulation of differential encoded signals in noisy fading channels. We present an indicator matrix representation for differential encoded signals and the optimal HMM filter for demodulation. The filter requires O(N3) calculations per time iteration, where N is the number of message symbols. Decision feedback equalisation is investigated via coupling the optimal HMM filter for estimating the message, conditioned on estimates of the channel parameters, and a KF for estimating the channel states, conditioned on soft information message estimates. The particular differential encoding scheme examined in this paper is differential phase shift keying. However, the techniques developed can be extended to other forms of differential modulation. The channel model we use allows for multiplicative channel distortions and additive white Gaussian noise. Simulation studies are also presented.

Short-data recursive HMM parameter estimation for rapid vision-based aircraft heading estimation

Rapid recursive estimation of hidden Markov Model (HMM) parameters is important in applications that place an emphasis on the early availability of reasonable estimates (e.g. for change detection) rather than the provision of longer-term asymptotic properties (such as convergence, convergence rate, and consistency). In the context of vision- based aircraft (image-plane) heading estimation, this paper suggests and evaluates the short-data estimation properties of 3 recursive HMM parameter estimation techniques (a recursive maximum likelihood estimator, an online EM HMM estimator, and a relative entropy based estimator). On both simulated and real data, our studies illustrate the feasibility of rapid recursive heading estimation, but also demonstrate the need for careful step-size design of HMM recursive estimation techniques when these techniques are intended for use in applications where short-data behaviour is paramount.

Can synchronous online counselling increase uptake of counselling services in secondary schools and what can promote or inhibit implementation?

This research showed that online counselling has the potential to increase the help-seeking of secondary school students - especially those who suffer from high levels of psychological distress. An investigation of why school counsellors are currently reluctant to provide an online counselling service identified a number of barriers to implementing such a potentially vital service. Response to focus groups and surveys completed by students and school counsellors indicated that more distressed students prefer to use online counselling and they would use it for sensitive topics. School counsellors remain concerned about effectiveness, ethical, legal and privacy issues as well as potential misuse of the service. Recommendations for implementation are made.

An improved robust field weakening algorithm for direct torque controlled synchronous reluctance motor drives

This paper presents an improved field weakening algorithm for synchronous reluctance motor (RSMs) drives. The proposed algorithm is robust to the variations in the machine d- and q-axes inductances. The transition between the maximum torque per ampere (MTPA), current and voltage limits as well as the maximum torque per flux (MTPF) trajectories is smooth. The proposed technique is combined with the direct torque control method to attain a high performance drive in the field weakening region. Simulation and experimental results are supplemented to verify the effectiveness of the proposed approach.

Towards strongly consistent online HMM parameter estimation using one-step Kerridge inaccuracy

In this paper, we propose a novel online hidden Markov model (HMM) parameter estimator based on the new information-theoretic concept of one-step Kerridge inaccuracy (OKI). Under several regulatory conditions, we establish a convergence result (and some limited strong consistency results) for our proposed online OKI-based parameter estimator. In simulation studies, we illustrate the global convergence behaviour of our proposed estimator and provide a counter-example illustrating the local convergence of other popular HMM parameter estimators.

Cross database training of audio-visual hidden Markov models for phone recognition

Speech recognition can be improved by using visual information in the form of lip movements of the speaker in addition to audio information. To date, state-of-the-art techniques for audio-visual speech recognition continue to use audio and visual data of the same database for training their models. In this paper, we present a new approach to make use of one modality of an external dataset in addition to a given audio-visual dataset. By so doing, it is possible to create more powerful models from other extensive audio-only databases and adapt them on our comparatively smaller multi-stream databases. Results show that the presented approach outperforms the widely adopted synchronous hidden Markov models (HMM) trained jointly on audio and visual data of a given audio-visual database for phone recognition by 29% relative. It also outperforms the external audio models trained on extensive external audio datasets and also internal audio models by 5.5% and 46% relative respectively. We also show that the proposed approach is beneficial in noisy environments where the audio source is affected by the environmental noise.