KEYWORDS: Sensors, Fusion energy, Sensor networks, Data processing, Digital signal processing, Data communications, Head, Signal detection, Helium, Network architectures
This paper presents a systematic approach to the design and implementation of an energy-efficient multi-sensor network. The nodes of the sensor network form the basis of a sectioned Bayesian network that can be used to determine the state of the system being monitored. A key issue in the design of Bayesian networks for monitoring engineering systems is to ensure that reliable inference scheme about the health state of the system can be made by combining information acquired from each sensor in the system into a single Bayesian network. As the size of the network increases, aggregating information made by all the sensors becomes computationally intractable. Hence, sectioning of the Bayesian network based on functional or logical constraints allows computational efficiency in aggregating information and reduces overall communication requirements. Furthermore, an in-network data processing scheme, motivated by the concept of Dynamic Voltage Scheduling, has been investigated to minimize computation energy consumption through dynamically adjusting the voltage supply and clock frequency of the individual sensors. As a result, the processor idle time can be better utilized for prolonged computation latency, leading to significantly reduced energy cost and increased computational efficiency.
KEYWORDS: Sensors, Data processing, Sensor networks, Data communications, Digital signal processing, Fusion energy, Head, Telecommunications, Data acquisition, Clocks
Efficiently utilizing the power available to increase service life of sensors is one of the key challenges in the design and operation of a wireless sensor network for system health monitoring. This paper addresses energy-efficient computation on the sensor node level by presenting an in-network data processing scheme. The scheme is motivated by the concept of Dynamic Voltage Scheduling (DVS), which minimizes energy consumption through dynamically adjusting the voltage supply and clock frequency of the individual sensors. Unlike the traditional approach where a uniform data processing speed is employed for all the sensors, the proposed scheme adjusts the speed of each sensor individually to utilize the processor idle time for prolonged computation latency. The advantage of such a scheme becomes increasingly evident when a large amount of raw data needs to be processed locally at each sensor to reduce the amount of overall data communication. An application model using vibration-base sensory nodes for machine health monitoring was constructed to test the new data processing scheme. Simulation has shown that energy saving of up to 29% could be achieved.
KEYWORDS: Wavelets, Wavelet transforms, Digital filtering, Electronic filtering, Fourier transforms, Filtering (signal processing), Signal analyzers, Signal to noise ratio, Sensors, Signal processing
This paper presents an adaptive filtering technique for the health diagnosis of mechanical systems, based on the generalized harmonic wavelet transformation. Through selection of two wavelet level parameters, a series of sub-frequency band wavelet coefficients corresponding to equi-bandwidth vibration signals measured from a machine were constructed. The energy and entropy associated with each sub-frequency band were then calculated, and the band with the maximum energy-to-entropy ratio was chosen to form a band-limited filter for the vibration signals. Experimental studies using rolling bearings that contain structural defects have confirmed that, the developed new technique enables high signal-to-noise ratio for effective machine failure detection and health diagnosis.
This paper presents the design, analysis, and experimental verification of a piezoelectric device that extracts energy from low-level vibrations. Such a device may be configured as a new source of power supply to operate wireless sensor networks. A millimeter-sized, non-uniformly shaped beam consisting of two piezoelectric layers is proposed as the key component of the device. An analytical model of the beam is established and used to predict the dynamic response of the beam and subsequently, its power output, when it is subject to vibration inputs. Through a coupled-field analysis, the coupling between the mechanical and electrical domains of the energy extraction device is analyzed. Simulations and experiments on a vibration shaker have shown that, compared with the rectangular beam design that has been traditionally used, the new design has increased the energy extraction capability of the beam by as much as 70%. In addition to beam design, issues related to device packaging are also addressed in the paper.
KEYWORDS: Transmitters, Energy efficiency, Receivers, Sensors, Systems modeling, Modulators, Sensing systems, Ultrasonography, Signal detection, Ultrasonics
This paper presents a Bond graph approach to analyzing the energy efficiency of a self-powered wireless pressure sensor for pressure measurement in an injection mold. The sensor is located within the mold cavity and consists of an energy converter, an energy modulator, and a signal transmitter. Pressure variation in the mold cavity is extracted by the energy converter and transmitted through the mold steel in the form of ultrasound pulses to a signal receiver located outside of the mold. Through Bond graph models, the energy efficiency of the sensing system is characterized as a function of the configuration of the piezoceramic stack within the energy converter, and the pulsing cycle of the energy modulator. The obtained energy model is then used to identify the minimum level of signal intensity required to ensure successful detection of the ultrasound signals by the signal receiver. The Bond graph models established can be further used to optimize the design of the sensing system and its constituent components.
This paper presents a new approach to on-line health diagnosis of mechanical systems, based on the wavelet packet transform. Specifically, signals acquired from vibration sensors are decomposed into sub-bands by means of the discrete harmonic wavelet packet transform (DHWPT). Based on the Fisher linear discriminant criterion, features in the selected sub-bands are then used as inputs to three classifiers (Nearest Neighbor rule-based and two Neural Network-based), for system health condition assessment. Experimental results have confirmed that, comparing to the conventional approach where statistical parameters from raw signals are used, the presented approach enabled higher signal-to-noise ratio for more effective and intelligent use of the sensory information, thus leading to more accurate system health diagnosis.
A self-powered wireless sensing module for the condition monitoring of mechanical systems and high energy manufacturing processes is described, with injection molding as a special application. The design and analysis of three constituent components in such a sensing module: an energy converter consisting of a piezoceramic stack, an energy regulator based on a pair of bipolar transistors, and a piezoelectric transmitter that transmits ultrasound signals proportional to the pressure within the injection mold, are presented in this paper. The energy extraction mechanism is investigated based on the interactions between the mechanical strain and the electric field developed within the piezoceramic stack. Analytical models for the energy modulator and signal transmitter are also established. Quantitative results are obtained that describe the energy flow among the three components and guide the parametric design of the three constituent components. Simulations and experimental studies have validated the functionality of each component. The models established can be used to subsequently optimize the design of the entire sensor module in terms of minimizing the energy requirement for the sensor and identifying the minimum level of signal intensity required to ensure successful detection of the signal by the signal receiver on the outside of the injection mold. The proposed self-powered sensing technique enables a new generation of sensors that can be employed for the condition monitoring and health diagnosis of a wide range of mechanical and civil systems that are characterized by high energy contents.
KEYWORDS: Sensors, Signal attenuation, Signal to noise ratio, Signal detection, Wave propagation, Finite element methods, Wave sensors, Defect detection, Unattended ground sensors, Signal processing
This paper presents a systematic investigation of the effect of sensor placement on the measurement data quality and subsequently, on the effectiveness of machine system health monitoring. First, signal propagation process from the defect location to the sensor was analyzed. Numerical simulations using finite element modeling was then conducted to determine the signal strength at several representative sensor locations. The analytical and numerical results showed that placing sensors closer to the component being monitored resulted in higher signal-to-noise ratio, thus improving the data quality. Using meso-sized piezoceramic chips, the obtained results were then experimentally evaluated. Comparisons with a set of commercial vibration sensors verified the developed sensor placement strategy. The presented study quantitatively justified why placing sensors closer to the machine being monitored is of critical importance to ensuring high quality data, and confirmed that the customized shock wave sensing approach can achieve comparable result for vibration measurement, but with a much less space requirement.
Implementing a reliable condition monitoring system for bearing fault diagnosis and prognosis poses a big challenge to the industry. This challenge stems from the fact that bearing failure is statistical in nature, and thus contains elements of uncertainty and unpredictability. To achieve high accuracy in bearing diagnosis in spite of this inherent variance, reliable data acquisition and analysis techniques are needed. This paper focuses on the vibration analysis of a wireless transmitter module with an integrated sensor that is embedded into the bearing outer raceway for high signal-to-noise ratio data acquisition. A mechatronic design of the sensor module under severe space constraints is presented. The paper also analyses an optimizing scheme for the placement of sensor module substrate supports to reduce vibration transmitted from the bearing to the on-board electronics.
The detection of structural defects in a ball bearing using an embedded piezoceramic load sensor and the discrete wavelet transform is presented. A model to predict the output of the embedded sensor was developed and experimentally verified. A mother wavelet was developed specifically for analyzing the response of the bearing and embedded load sensor. This wavelet was found to produce a more descriptive decomposition of the sensor signal than a standard Daubechies wavelet. Furthermore, the relationship between a bearing misalignment and the resulting load variation was established.
Increasing demands for product safety and reliability requires the development and implementation of innovative condition monitoring mechanisms that are an integral part of the system to be monitored. Since an advanced condition monitoring system often consists of a variety of sensing, controlling, and actuating components, their effective and efficient integration requires the application of mechatronic design principles to achieve the desired synergy. This paper presents several aspects related to the design and implementation of a sensor-embedded mechatronic bearing, which can be used for the condition monitoring of various critical machine systems.
KEYWORDS: Sensors, Electronics, Electronic components, Mechatronics, Signal processing, Signal generators, Data modeling, Lead, Signal to noise ratio, Dynamical systems
To assess the working condition of a rolling element bearing, the condition monitoring system should be located as close as possible to the bearing to take advantage of shorter signal transmission path, increased signal-to-noise ratio, and reduced complexity of the signal processing electronics. The advantages of integrated sensing are presented in this paper, with a focus on the design and analysis of a miniaturized sensor module. Mechatronic principles have been applied to treat the various subjects in a synergistic way. To complement analytical studies, experiments have been conducted on a scaled-up version of the sensor module to analyze the system dynamic response. The result obtained provided insight into the electromechanical interaction within the module as well as input for the system implementation using miniaturization technologies.
Applying the principle of mechatronics, this paper presents design considerations for a new type of sensor-embedded 'smart' long cane which can serve as an orientation and travel aid for the blind. The smart long cane uses a set of miniaturized ultrasonic sensors to detect obstacles along the travel path, and provide human voice feedback on the obstacle's height and distance to guide the cane user away for head collisions and subsequent injuries. Topics discussed include sensor array design, placement, and integration, structure of the embedded software, and ergonomic issues for the new cane design.
This paper analyzes various design considerations of a 'smart' rolling element bearing with self-diagnostic capabilities. The self-diagnostic capability was added to the bearing by integrating a condition monitoring module into the raceway. The advantages of this design over a conventional condition monitoring system are discussed. A structural analysis was done to evaluate the effect of various modifications to the bearing raceway. It was found that the raceway of a conventional bearing could accept a miniaturized sensor module without a significant impact to the bearing. A miniaturized acoustic emission sensor was designed to fit into the sensor module. The sensor was designed to respond to the bearing's characteristic frequencies.
Ultrasonic transducers having curved radiating surfaces may offer a simple solution to maintaining good lateral resolution over the large depth of field required in medical imaging. In this paper the design considerations for such a transducer that consists of a cylindrical metal housing and an ultrasonic wave generating piezoceramic disc is presented. The mechanism of focusing the radiated ultrasonic wave is studied by changing the geometry of the front surface of the metal housing. The propagation of ultrasonic wave in the surrounding medium is analyzed using the impulse response approach for the near field region and Fraunhofer's approximation for the far field. In addition, modal analysis of the transducer structure is conducted using the finite element method. The results obtained show that the geometry of the transducer housing has significant effects on the radiation characteristics of the transducer.
A series of millimeter-sized, addressable linear and rotary surface-driven electrostatic positioners are currently being designed and fabricated. The major components of these positioners are a thin copper-coated glass epoxy stator board and a carbon-coated polymer film slider which is placed on top of the stator board. Using modified Printed Circuit Board technique, a group of conductive electrodes are linearly or radially arranged on the stator board. On application of an excitation voltage pattern to the stator electrodes, a mirror image of the stator electrical charges will be induced on the film slider. Sequential switching of the voltage pattern will lead to electrical charge interactions, resulting in continuous motion of the film slider. Compared to the electromagnetic counterparts, these electrostatics-based positioners do not require the conventional mechanical assembly of transmission gears and rails for operation, thus are compact in design and light in weight. The potential advantage of low manufacturing cost may help this new type of positioners find a wide range of industrial, military, and commercial applications.
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