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.

Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests

The use of Wireless Sensor Networks (WSNs) for vibration-based Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data asynchronicity and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. Based on a brief review, this paper first reveals that Data Synchronization Error (DSE) is the most inherent factor amongst uncertainties of SHM-oriented WSNs. Effects of this factor are then investigated on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when merging data from multiple sensor setups. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as benchmark data after being added with a certain level of noise to account for the higher presence of this factor in SHM-oriented WSNs. From this source, a large number of simulations have been made to generate multiple DSE-corrupted datasets to facilitate statistical analyses. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with DSE at a relaxed level. Finally, the combination of preferred OMA techniques and the use of the channel projection for the time-domain OMA technique to cope with DSE are recommended.

Elliptical summary randomisation for sensor-based human activity recognition

Many conventional statistical machine learning al- gorithms generalise poorly if distribution bias ex- ists in the datasets. For example, distribution bias arises in the context of domain generalisation, where knowledge acquired from multiple source domains need to be used in a previously unseen target domains. We propose Elliptical Summary Randomisation (ESRand), an efficient domain generalisation approach that comprises of a randomised kernel and elliptical data summarisation. ESRand learns a domain interdependent projection to a la- tent subspace that minimises the existing biases to the data while maintaining the functional relationship between domains. In the latent subspace, ellipsoidal summaries replace the samples to enhance the generalisation by further removing bias and noise in the data. Moreover, the summarisation enables large-scale data processing by significantly reducing the size of the data. Through comprehensive analysis, we show that our subspace-based approach outperforms state-of-the-art results on several activity recognition benchmark datasets, while keeping the computational complexity significantly low.

Compact fiber Bragg grating dynamic strain sensor cum broadband thermometer for thermally unstable ambience

An instrument for simultaneous measurement of dynamic strain and temperature in a thermally unstable ambience has been proposed, based on fiber Bragg grating technology. The instrument can function as a compact and stand-alone broadband thermometer and a dynamic strain gauge. It employs a source wavelength tracking procedure for linear dependence of the output on the measurand, offering high dynamic range. Two schemes have been demonstrated with their relative merits. As a thermometer, the present instrumental configuration can offer a linear response in excess of 500 degrees C that can be easily extended by adding a suitable grating and source without any alteration in the procedure. Temperature sensitivity is about 0.06 degrees C for a bandwidth of 1 Hz. For the current grating, the upper limit of strain measurement is about 150 mu epsilon with a sensitivity of about 80 n epsilon Hz(-1/2). The major source of uncertainty associated with dynamic strain measurement is the laser source intensity noise, which is of broad spectral band. A low noise source device or the use of optical power regulators can offer improved performance. The total harmonic distortion is less than 0.5% up to about 50 mu epsilon, 1.2% at 100 mu epsilon and about 2.3% at 150 mu epsilon. Calibrated results of temperature and strain measurement with the instrument have been presented. Traces of ultrasound signals recorded by the system at 200 kHz, in an ambience of 100-200 degrees C temperature fluctuation, have been included. Also, the vibration spectrum and engine temperature of a running internal combustion engine has been recorded as a realistic application of the system.

Comparison of the sensitivity of different sensor technologies to imbalance severity in low speed wind turbines

Imbalance is not only a direct major cause of downtime in wind turbines, but also accelerates the degradation of neighbouring and downstream components (e.g. main bearing, generator). Along with detection, the imbalance quantification is also essential as some residual imbalance always exist even in a healthy turbine. Three different commonly used sensor technologies (vibration, acoustic emission and electrical measurements) are investigated in this work to verify their sensitivity to different imbalance grades. This study is based on data obtained by experimental tests performed on a small scale wind turbine drive train test-rig for different shaft speeds and imbalance levels. According to the analysis results, electrical measurements seem to be the most suitable for tracking the development of imbalance.

On the Average Case Communication Complexity for Detection in Sensor Networks

The problem of sensor-network-based distributed intrusion detection in the presence of clutter is considered. It is argued that sensing is best regarded as a local phenomenon in that only sensors in the immediate vicinity of an intruder are triggered. In such a setting, lack of knowledge of intruder location gives rise to correlated sensor readings. A signal-space viewpoint is introduced in which the noise-free sensor readings associated to intruder and clutter appear as surfaces $\mathcal{S_I}$ and $\mathcal{S_C}$ and the problem reduces to one of determining in distributed fashion, whether the current noisy sensor reading is best classified as intruder or clutter. Two approaches to distributed detection are pursued. In the first, a decision surface separating $\mathcal{S_I}$ and $\mathcal{S_C}$ is identified using Neyman-Pearson criteria. Thereafter, the individual sensor nodes interactively exchange bits to determine whether the sensor readings are on one side or the other of the decision surface. Bounds on the number of bits needed to be exchanged are derived, based on communication complexity (CC) theory. A lower bound derived for the two-party average case CC of general functions is compared against the performance of a greedy algorithm. The average case CC of the relevant greater-than (GT) function is characterized within two bits. In the second approach, each sensor node broadcasts a single bit arising from appropriate two-level quantization of its own sensor reading, keeping in mind the fusion rule to be subsequently applied at a local fusion center. The optimality of a threshold test as a quantization rule is proved under simplifying assumptions. Finally, results from a QualNet simulation of the algorithms are presented that include intruder tracking using a naive polynomial-regression algorithm.

Simple kinetic sensor to structural transitions

Driven nonequilibrium structural phase transformation has been probed using time-varying resistance fluctuations or noise. We demonstrate that the non-Gaussian component (NGC) of noise obtained by evaluating the higher-order statistics of fluctuations, serves as a simple kinetic detector of these phase transitions. Using the Martensite transformation in free-standing wires of nickel-titanium binary alloys as a prototype, we observe clear deviations from the Gaussian background in the transformation zone, indicative of the long-range correlations in the system as the phase transforms. The viability of non-Gaussian statistics as a robust probe to structural phase transition was also confirmed by comparing the results from differential scanning calorimetry measurements. We further studied the response of the NGC to the modifications in the microstructure on repeated thermal cycling, as well as the variations in the temperature-drive rate, and explained the results using established simplistic models based on the different competing time scales. Our experiments (i) suggest an alternative method to estimate the transformation temperature scales with high accuracy and (ii) establish a connection between the material-specific evolution of microstructure to the statistics of its linear response. Since the method depends on an in-built long-range correlation during transformation, it could be portable to other structural transitions, as well as to materials of different physical origin and size.

Noise measurements of a phase fluorometric instrument

We present noise measurements of a phase fluorometric oxygen sensor that sets the limits of accuracy for this instrument. We analyze the phase sensitive detection measurement system with the signal ''shot'' noise being the only significant contribution to the system noise. Based on the modulated optical power received by the photomultiplier, the analysis predicts a noise spectral power density that was within 3 dB of the measured power spectral noise density. Our results demonstrate that at a received optical power of 20 fW the noise level was low enough to permit the detection of a change oxygen concentration of 1% at the sensor. We also present noise measurements of a new low-cost version of this instrument that uses a photodiode instead of a photomultiplier. These measurements show that the noise for this instrument was limited by noise generated in the preamplifier following the photodiode. (C) 1996 Society of Photo-Optical Instrumentation Engineers.

Distributed intrusion detection in the presence of correlated sensor readings: Signal-space and communication-complexity view-point

The problem of sensor-network-based distributed intrusion detection in the presence of clutter is considered. It is argued that sensing is best regarded as a local phenomenon in that only sensors in the immediate vicinity of an intruder are triggered. In such a setting, lack of knowledge of intruder location gives rise to correlated sensor readings. A signal-space view-point is introduced in which the noise-free sensor readings associated to intruder and clutter appear as surfaces f(s) and f(g) and the problem reduces to one of determining in distributed fashion, whether the current noisy sensor reading is best classified as intruder or clutter. Two approaches to distributed detection are pursued. In the first, a decision surface separating f(s) and f(g) is identified using Neyman-Pearson criteria. Thereafter, the individual sensor nodes interactively exchange bits to determine whether the sensor readings are on one side or the other of the decision surface. Bounds on the number of bits needed to be exchanged are derived, based on communication-complexity (CC) theory. A lower bound derived for the two-party average case CC of general functions is compared against the performance of a greedy algorithm. Extensions to the multi-party case is straightforward and is briefly discussed. The average case CC of the relevant greaterthan (CT) function is characterized within two bits. Under the second approach, each sensor node broadcasts a single bit arising from appropriate two-level quantization of its own sensor reading, keeping in mind the fusion rule to be subsequently applied at a local fusion center. The optimality of a threshold test as a quantization rule is proved under simplifying assumptions. Finally, results from a QualNet simulation of the algorithms are presented that include intruder tracking using a naive polynomial-regression algorithm. 2010 Elsevier B.V. All rights reserved.

Comparative Analysis of Pilot-Assisted Distributed Cophasing Approaches in Wireless Sensor Networks

This paper compares and analyzes the performance of distributed cophasing techniques for uplink transmission over wireless sensor networks. We focus on a time-division duplexing approach, and exploit the channel reciprocity to reduce the channel feedback requirement. We consider periodic broadcast of known pilot symbols by the fusion center (FC), and maximum likelihood estimation of the channel by the sensor nodes for the subsequent uplink cophasing transmission. We assume carrier and phase synchronization across the participating nodes for analytical tractability. We study binary signaling over frequency-flat fading channels, and quantify the system performance such as the expected gains in the received signal-to-noise ratio (SNR) and the average probability of error at the FC, as a function of the number of sensor nodes and the pilot overhead. Our results show that a modest amount of accumulated pilot SNR is sufficient to realize a large fraction of the maximum possible beamforming gain. We also investigate the performance gains obtained by censoring transmission at the sensors based on the estimated channel state, and the benefits obtained by using maximum ratio transmission (MRT) and truncated channel inversion (TCI) at the sensors in addition to cophasing transmission. Simulation results corroborate the theoretical expressions and show the relative performance benefits offered by the various schemes.

Analysis of a Novel MOEM Race Track Resontor based Vibration Sensor

In this paper we analyze a novel Micro Opto Electro Mechanical Systems (MOEMS) race track resonator based vibration sensor. In this vibration sensor the straight portion of a race track resonator is located at the foot of the cantilever beam with proof mass. As the beam deflects due to vibration, stress induced refractive change in the waveguide located over the beam lead to the wavelength shift providing the measure of vibration. A wavelength shift of 3.19 pm/g in the range of 280 g for a cantilever beam of 1750μm×450m×20μmhas been obtained. The maximum acceleration (breakdown) for these dimensions is 2900g when a safety factor of 2 is taken into account. Since the wavelength of operation is around 1.55μm hybrid integration of source and detector is possible on the same substrate. Also it is less amenable to noise as wavelength shift provides the sensor signal. This type of sensors can be used for aerospace application and other harsh environments with suitable design.

AEROSAT - a space-borne sensor for continental aerosols: evaluation of the conceptual model

Even though satellite observations are the most effective means to gather global information in a short span of time, the challenges in this field still remain over continental landmass, despite most of the aerosol sources being land-based. This is a hurdle in global and regional aerosol climate forcing assessment. Retrieval of aerosol properties over land is complicated due to irregular terrain characteristics and the high and largely uncertain surface reflection which acts as `noise' to the much smaller amount of radiation scattered by aerosols, which is the `signal'. In this paper, we describe a satellite sensor the - `Aerosol Satellite (AEROSAT)', which is capable of retrieving aerosols over land with much more accuracy and reduced dependence on models. The sensor, utilizing a set of multi-spectral and multi-angle measurements of polarized components of radiation reflected from the Earth's surface, along with measurements of thermal infrared broadband radiance, results in a large reduction of the `noise' component (compared to the `signal). A conceptual engineering model of AEROSAT has been designed, developed and used to measure the land-surface features in the visible spectral band. Analysing the received signals using a polarization radiative transfer approach, we demonstrate the superiority of this method. It is expected that satellites carrying sensors following the AEROSAT concept would be `self-sufficient', to obtain all the relevant information required for aerosol retrieval from its own measurements.

A power-scalable RF CMOS receiver for 2.4 GHz Wireless Sensor Network applications

A power scalable receiver architecture is presented for low data rate Wireless Sensor Network (WSN) applications in 130nm RF-CMOS technology. Power scalable receiver is motivated by the ability to leverage lower run-time performance requirement to save power. The proposed receiver is able to switch power settings based on available signal and interference levels while maintaining requisite BER. The Low-IF receiver consists of Variable Noise and Linearity LNA, IQ Mixers, VGA, Variable Order Complex Bandpass Filter and Variable Gain and Bandwidth Amplifier (VGBWA) capable of driving variable sampling rate ADC. Various blocks have independent power scaling controls depending on their noise, gain and interference rejection (IR) requirements. The receiver is designed for constant envelope QPSK-type modulation with 2.4GHz RF input, 3MHz IF and 2MHz bandwidth. The chip operates at 1V Vdd with current scalable from 4.5mA to 1.3mA and chip area of 0.65mm2.

A Vision-Based Micro-Newton Static Force Sensor Using a Displacement-Amplifying Compliant Mechanism (DaCM)

A micro-newton static force sensor is presented here as a packaged product. The sensor, which is based on the mechanics of deformable objects, consists of a compliant mechanism that amplifies the displacement caused by the force that is to be measured. The output displacement, captured using a digital microscope and analyzed using image processing techniques, is used to calculate the force using precalibrated force-displacement curve. Images are scanned in real time at a frequency of 15 frames per second and sampled at around half the scanning frequency. The sensor was built, packaged, calibrated, and tested. It has simulated and measured stiffness values of 2.60N/m and 2.57N/m, respectively. The smallest force it can reliably measure in the presence of noise is about 2 mu N over a range of 1.4mN. The off-the-shelf digital microscope aside, all of its other components are purely mechanical; they are inexpensive and can be easily made using simple machines. Another highlight of the sensor is that its movable and delicate components are easily replaceable. The sensor can be used in aqueous environment as it does not use electric, magnetic, thermal, or any other fields. Currently, it can only measure static forces or forces that vary at less than 1Hz because its response time and bandwidth are limited by the speed of imaging with a camera. With a universal serial bus (USB) connection of its digital microscope, custom-developed graphical user interface (GUI), and related software, the sensor is fully developed as a readily usable product.

A resonant micromachined electrostatic charge sensor

A micromachined electrometer, based on the concept of a variable capacitor, has been designed, modeled, fabricated, and tested. The device presented in this paper functions as a modulated variable capacitor, wherein a dc charge to be measured is up-modulated and converted to an ac voltage output, thus improving the signal-to-noise ratio. The device was fabricated in a commercial standard SOI micromachining process without the need for any additional processing steps. The electrometer was tested in both air and vacuum at room temperature. In air, it has a charge-to-voltage conversion gain of 2.06 nV/e, and a measured charge noise floor of 52.4 e/rtHz. To reduce the effects of input leakage current, an electrically isolated capacitor has been introduced between the variable capacitor and input to sensor electronics. Methods to improve the sensitivity and resolution are suggested while the long-term stability of these sensors is modeled and discussed. © 2006 IEEE.