A practical signal processing approach for condition monitoring of low speed machinery using Peak-Hold-Down-Sample algorithm

A simple and effective down-sample algorithm, Peak-Hold-Down-Sample (PHDS) algorithm is developed in this paper to enable a rapid and efficient data transfer in remote condition monitoring applications. The algorithm is particularly useful for high frequency Condition Monitoring (CM) techniques, and for low speed machine applications since the combination of the high sampling frequency and low rotating speed will generally lead to large unwieldy data size. The effectiveness of the algorithm was evaluated and tested on four sets of data in the study. One set of the data was extracted from the condition monitoring signal of a practical industry application. Another set of data was acquired from a low speed machine test rig in the laboratory. The other two sets of data were computer simulated bearing defect signals having either a single or multiple bearing defects. The results disclose that the PHDS algorithm can substantially reduce the size of data while preserving the critical bearing defect information for all the data sets used in this work even when a large down-sample ratio was used (i.e., 500 times down-sampled). In contrast, the down-sample process using existing normal down-sample technique in signal processing eliminates the useful and critical information such as bearing defect frequencies in a signal when the same down-sample ratio was employed. Noise and artificial frequency components were also induced by the normal down-sample technique, thus limits its usefulness for machine condition monitoring applications.

The impact of anxiety on processing efficiency : implications for the attentional control theory

The Attentional Control Theory (ACT) proposes that high-anxious individuals maintain performance effectiveness (accuracy) at the expense of processing efficiency (response time), in particular, the two central executive functions of inhibition and shifting. In contrast, research has generally failed to consider the third executive function which relates to the function of updating. In the current study, seventy-five participants completed the Parametric Go/No-Go and n-back tasks, as well as the State-Trait Anxiety Inventory in order to explore the effects of anxiety on attention. Results indicated that anxiety lead to decay in processing efficiency, but not in performance effectiveness, across all three Central Executive functions (inhibition, set-shifting and updating). Interestingly, participants with high levels of trait anxiety also exhibited impaired performance effectiveness on the n-back task designed to measure the updating function. Findings are discussed in relation to developing a new model of ACT that also includes the role of preattentive processes and dual-task coordination when exploring the effects of anxiety on task performance.

Fiber Bragg grating strain modulation based on nonlinear string transverse force amplifier

The only effective method of Fiber Bragg Grating (FBG) strain modulation has been by changing the distance between its two fixed ends. We demonstrate an alternative being more sensitive to force based on the nonlinear amplification relationship between a transverse force applied to a stretched string and its induced axial force. It may improve the sensitivity and size of an FBG force sensor, reduce the number of FBGs needed for multi-axial force monitoring, and control the resonant frequency of an FBG accelerometer.

Static vertical displacement measurement of bridges using Fiber Bragg Grating (FBG) sensors

Vertical displacements are one of the most relevant parameters for structural health monitoring of bridges in both the short and long terms. Bridge managers around the globe are always looking for a simple way to measure vertical displacements of bridges. However, it is difficult to carry out such measurements. On the other hand, in recent years, with the advancement of fiber-optic technologies, fiber Bragg grating (FBG) sensors are more commonly used in structural health monitoring due to their outstanding advantages including multiplexing capability, immunity of electromagnetic interference as well as high resolution and accuracy. For these reasons, using FBG sensors is proposed to develop a simple, inexpensive and practical method to measure vertical displacements of bridges. A curvature approach for vertical displacement measurements using curvature measurements is proposed. In addition, with the successful development of FBG tilt sensors, an inclination approach is also proposed using inclination measurements. A series of simulation tests of a full- scale bridge was conducted. It shows that both of the approaches can be implemented to determine vertical displacements for bridges with various support conditions, varying stiffness (EI) along the spans and without any prior known loading. These approaches can thus measure vertical displacements for most of slab-on-girder and box-girder bridges. Besides, the approaches are feasible to implement for bridges under various loading. Moreover, with the advantages of FBG sensors, they can be implemented to monitor bridge behavior remotely and in real time. A beam loading test was conducted to determine vertical displacements using FBG strain sensors and tilt sensors. The discrepancies as compared with dial gauges reading using the curvature and inclination approaches are 0.14mm (1.1%) and 0.41mm (3.2%), respectively. Further recommendations of these approaches for developments will also be discussed at the end of the paper.

Partial melt processing and electrical properties of Bi-Sr-Ca-Cu-o superconducting thick films on (100) MgO substrates

Superconducting thick films of Bi2Sr2CaCu2Oy (Bi-2212) on single-crystalline (100) MgO substrates have been prepared using a doctor-blade technique and a partial-melt process. It is found that the phase composition and the amount of Ag addition to the paste affect the structure and superconducting properties of the partially melted thick films. The optimum heat treatment schedule for obtaining high Jc has been determined for each paste. The heat treatment ensures attainment of high purity for the crystalline Bi-2212 phase and high orientation of Bi-2212 crystals, in which the c-axis is perpendicular to the substrate. The highest Tc, obtained by resistivity measurement, is 92.2 K. The best value for Jct (transport) of these thick films, measured at 77 K in self-field, is 8 × 10 3 Acm -2.

Preliminary study on the stability of highly sensitive fiber Bragg grating temperature sensors [对高灵敏光纤光栅温度传感器的稳定性的初探]

As a novel sensitive element and due to its advantages of immunity to electrical interference, distributed measurement, etc., fiber Bragg grating (FBG) has been researched widely. To realize the substitution of high accurate electronic temperature sensors, high sensitive FBG temperature sensors can be made by taking advantage of its characters of being sensitive to both temperature and strain. Although there are reports about high sensitive FBG temperature sensors, however, few about their stability have been done. We manufactured a high sensitive FBG temperature sensor, and put it together with an average FBG temperature sensor and an electronic crystal temperature sensor into a stainless steel container filled by water to observe the room temperature change. By comparing their results in two weeks, we have found out that: although the high sensitive FBG temperature sensor is in much better agreement with the electronic crystal sensor than the average FBG sensor is, it has occurred some small drifts. Because the drifts appeared in the process of further pulling the FBG, it might be a result of the slip of the FBG fixing points. This contributes some good experiences to the application of FBG in high accuracy temperature measurement.

High-sensitivity fiber Bragg grating temperature sensor at high temperature [一种高温下高灵敏光纤光栅温度传感器的制作方法]

A method of making full use of the durable strain which fiber Bragg grating (FBG) can undertake is presented, which hugely improves the sensitivities of FBG temperature sensors at high temperature. When a sensor is manufactured at room temperature, its FBG should be given a pre-relaxing length according to the temperature it is asked to measure; once the temperature rise to the asked one, its FBG starts to be stretched and it starts to work with high sensitivity. The relationship between the pre-relaxing length and the working temperature is analyzed. In experiments, when the pre-relaxing lengths are 0.2mm、0.5mm、0.6mm, the working temperatures rise 25℃、50℃、61℃, respectively, and the sensitivities are almost the same (675pm/℃). The facts that the experimental results agree well with the theoretical analyses verify this method’s validity.

A fiber Bragg grating temperature sensor with adjustable sensitivity [一种灵敏度系数可调的光纤光栅温度传感器]

As a novel sensing element, fiber Bragg grating (FBG) is sensitive to both temperature and strain. Basing on this character, high sensitivity FBG temperature sensor can be made. However, as a result of the strain limit of the fiber, the temperature range it can endure is quite narrow. This drawback limits its application and complicates its storage and transport. We design and manufacture a FBG temperature sensor with tunable sensitivity. By tuning its sensitivity, its temperature range is changed, which enlarges its application field, solves the problem of storage and transport, and brighten the future of FBG in temperature measurement. In experiment, by changing the fixing position of the bimetal we tuned the sensitivity of the high sensitivity FBG sensor to different values (-47 pm/℃，-97.7 pm/℃，-153.3 pm/℃).

The application of fiber Bragg grating sensor to high precision temperature measurement [基于光纤光栅的高精度测温传感器研究]

Basing on the character that Fiber Bragg Grating (FBG) is sensitive to both temperature and strain, by using Al and Fe-Ni alloy’s bimetal structure, we successfully design and manufacture a high accuracy FBG temperature sensor for earthquake premonition. Furthermore, we analyze the accuracy of the FBG sensors with enhanced sensitivity for the first time, and get its accuracy is up to ±0.05℃ with highest resolution ever in all FBG temperature sensors (0.0014℃/pm). This work experimentally proves the feasibility of using FBG in the earthquake premonition monitoring, and builds the foundation for the application of optic technology in earthquake premonition monitoring.

Social signal processing for pain monitoring using a hidden conditional random field

utomatic pain monitoring has the potential to greatly improve patient diagnosis and outcomes by providing a continuous objective measure. One of the most promising methods is to do this via automatically detecting facial expressions. However, current approaches have failed due to their inability to: 1) integrate the rigid and non-rigid head motion into a single feature representation, and 2) incorporate the salient temporal patterns into the classification stage. In this paper, we tackle the first problem by developing a “histogram of facial action units” representation using Active Appearance Model (AAM) face features, and then utilize a Hidden Conditional Random Field (HCRF) to overcome the second issue. We show that both of these methods improve the performance on the task of pain detection in sequence level compared to current state-of-the-art-methods on the UNBC-McMaster Shoulder Pain Archive.

Featureless visual processing for SLAM in changing outdoor environments

Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features.

The use of a Riesz fractional differential-based approach for texture enhancement in image processing

Abstract: Texture enhancement is an important component of image processing, with extensive application in science and engineering. The quality of medical images, quantified using the texture of the images, plays a significant role in the routine diagnosis performed by medical practitioners. Previously, image texture enhancement was performed using classical integral order differential mask operators. Recently, first order fractional differential operators were implemented to enhance images. Experiments conclude that the use of the fractional differential not only maintains the low frequency contour features in the smooth areas of the image, but also nonlinearly enhances edges and textures corresponding to high-frequency image components. However, whilst these methods perform well in particular cases, they are not routinely useful across all applications. To this end, we applied the second order Riesz fractional differential operator to improve upon existing approaches of texture enhancement. Compared with the classical integral order differential mask operators and other fractional differential operators, our new algorithms provide higher signal to noise values, which leads to superior image quality.

Digital social signal processing - theorectical underpinning and research agenda

Organizations make increasingly use of social media in order to compete for customer awareness and improve the quality of their goods and services. Multiple techniques of social media analysis are already in use. Nevertheless, theoretical underpinnings and a sound research agenda are still unavailable in this field at the present time. In order to contribute to setting up such an agenda, we introduce digital social signal processing (DSSP) as a new research stream in IS that requires multi-facetted investigations. Our DSSP concept is founded upon a set of four sequential activities: sensing digital social signals that are emitted by individuals on social media; decoding online data of social media in order to reconstruct digital social signals; matching the signals with consumers’ life events; and configuring individualized goods and service offerings tailored to the individual needs of customers. We further contribute to tying loose ends of different research areas together, in order to frame DSSP as a field for further investigation. We conclude with developing a research agenda.

Very sensitive fiber Bragg grating accelerometer using transverse forces with an easy over-range protection and low cross axial sensitivity

The first fiber Bragg grating (FBG) accelerometer using direct transverse forces is demonstrated by fixing the FBG by its two ends and placing a transversely moving inertial object at its middle. It is very sensitive because a lightly stretched FBG is more sensitive to transverse forces than axial forces. Its resonant frequency and static sensitivity are analyzed by the classic spring-mass theory, assuming the axial force changes little. The experiments show that the theory can be modified for cases where the assumption does not hold. The resonant frequency can be modified by a linear relationship experimentally achieved, and the static sensitivity by an alternative method proposed. The principles of the over-range protection and low cross axial sensitivity are achieved by limiting the movement of the FBG and were validated experimentally. The sensitivities 1.333 and 0.634 nm/g were experimentally achieved by 5.29 and 2.83 gram inertial objects at 10 Hz from 0.1 to 0.4 g (g = 9.8 m/s 2), respectively, and their resonant frequencies were around 25 Hz. Their theoretical static sensitivities and resonant frequencies found by the modifications are 1.188 nm/g and 26.81 Hz for the 5.29 gram one and 0.784 nm/g and 29.04 Hz for the 2.83 gram one, respectively.

Distributed low-power image processing in wireless sensor networks for intelligent video surveillance applications

Distributed Wireless Smart Camera (DWSC) network is a special type of Wireless Sensor Network (WSN) that processes captured images in a distributed manner. While image processing on DWSCs sees a great potential for growth, with its applications possessing a vast practical application domain such as security surveillance and health care, it suffers from tremendous constraints. In addition to the limitations of conventional WSNs, image processing on DWSCs requires more computational power, bandwidth and energy that presents significant challenges for large scale deployments. This dissertation has developed a number of algorithms that are highly scalable, portable, energy efficient and performance efficient, with considerations of practical constraints imposed by the hardware and the nature of WSN. More specifically, these algorithms tackle the problems of multi-object tracking and localisation in distributed wireless smart camera net- works and optimal camera configuration determination. Addressing the first problem of multi-object tracking and localisation requires solving a large array of sub-problems. The sub-problems that are discussed in this dissertation are calibration of internal parameters, multi-camera calibration for localisation and object handover for tracking. These topics have been covered extensively in computer vision literatures, however new algorithms must be invented to accommodate the various constraints introduced and required by the DWSC platform. A technique has been developed for the automatic calibration of low-cost cameras which are assumed to be restricted in their freedom of movement to either pan or tilt movements. Camera internal parameters, including focal length, principal point, lens distortion parameter and the angle and axis of rotation, can be recovered from a minimum set of two images of the camera, provided that the axis of rotation between the two images goes through the camera's optical centre and is parallel to either the vertical (panning) or horizontal (tilting) axis of the image. For object localisation, a novel approach has been developed for the calibration of a network of non-overlapping DWSCs in terms of their ground plane homographies, which can then be used for localising objects. In the proposed approach, a robot travels through the camera network while updating its position in a global coordinate frame, which it broadcasts to the cameras. The cameras use this, along with the image plane location of the robot, to compute a mapping from their image planes to the global coordinate frame. This is combined with an occupancy map generated by the robot during the mapping process to localised objects moving within the network. In addition, to deal with the problem of object handover between DWSCs of non-overlapping fields of view, a highly-scalable, distributed protocol has been designed. Cameras that follow the proposed protocol transmit object descriptions to a selected set of neighbours that are determined using a predictive forwarding strategy. The received descriptions are then matched at the subsequent camera on the object's path using a probability maximisation process with locally generated descriptions. The second problem of camera placement emerges naturally when these pervasive devices are put into real use. The locations, orientations, lens types etc. of the cameras must be chosen in a way that the utility of the network is maximised (e.g. maximum coverage) while user requirements are met. To deal with this, a statistical formulation of the problem of determining optimal camera configurations has been introduced and a Trans-Dimensional Simulated Annealing (TDSA) algorithm has been proposed to effectively solve the problem.