Antonio Pietrosanto

Balancing computational and transmission power consumption in wireless image sensor networks

The paper presents a methodology to reduce the energy consumption of visual sensor node in wireless sensor networks. This methodology, based on a balancing between processing and transmission tasks, can be applied in all those situations where subjective image based measurements are required. Considering that in a sensor node, the communication task is the most energy consumption, the proposed method tries to find the best compression ratio able to give a significant reduction of transmission time, returning adequate and objective image quality and compression time. Some results are presented applying the proposed methodology to the particular case study: the use of wireless visual sensors to the remote metering of water counters.

A DSP-based FFT-analyzer for the fault diagnosis of rotating machine based on vibration analysis

A DSP-based measurement system dedicated to the vibration analysis on rotating machines was designed and realized. Vibration signals are on-line acquired and processed to obtain a continuous monitoring of the machine status. In case of fault, the system is capable of isolating the fault with a high reliability. The paper describes in detail the approach followed to built up fault and unfault models together with the chosen hardware and software solutions. A number of tests carried out on small-size three-phase asynchronous motors highlights high promptness in detecting faults, low false alarm rate, and very good diagnostic performance.

An automatic measurement system for the evaluation of carotid intima-media thickness

This paper presents an automatic system for the measurement of carotid intima-media thickness (IMT) that is based on the digital processing of ultrasound images. The measurement technique is described in detail, highlighting the advantages compared to other methods and, reporting some experimental results. Finally, an analytical approach is used to estimate the intrinsic accuracy of the system.

Instrument fault detection and isolation: state of the art and new research trends

The paper presents the state of the art of residual generation techniques adopted in instrument fault detection and isolation. Both traditional and innovative methods are described evidencing their advantages and their limits. The improvement of analytical redundancy technique performances for better dealing with high-dynamic systems and/or with on-line applications are pointed out as the most interesting needs on which to focus the research efforts.

Propagation of uncertainty in a discrete Fourier transform algorithm

Abstract The problem of evaluating the uncertainty that characterises discrete Fourier transform output data is dealt with, using a method based on a ‘white box’ theoretical approach. The main sources of uncertainty (quantization, time jitter, microprocessor finite wordlength) are analysed obtaining equations useful to evaluate the uncertainty in both module and phase output values, for any hardware configuration and for any algorithm operating condition. The theoretical results, verified by both simulation and experimental tests, can be particularly useful for any designer and user of DFT-based instruments, since they allow the measurement configuration to be optimised with respect to the final uncertainty.

On-line sensor fault detection, isolation, and accommodation in automotive engines

The paper describes the hybrid solution, based on Artificial Neural Networks, ANNs, and production rule adopted in the realization of an Instrument Fault Detection, Isolation, and Accommodation scheme for automotive applications. Details on the ANN architectures and training are given together with diagnostic and dynamic performance of the scheme.

A neural network approach to instrument fault detection and isolation

The growing diffusion of Artificial Neural Network (ANN) applications suggests the authors a possible solution to Instrument Fault Detection and Isolation (IFDI) problems. It is based on the modelling of both the measurement station and the system under analysis by a suitable ANN, having the input layer fed by instrument outputs and the output layer which gives information for faulty instrument detection and isolation. The methodologies adopted are described in detail and tested on a complex automatic measurement station for induction motor testing. The performance of the proposed IFDI scheme is experimentally evaluated mainly in terms of correct diagnosis, incorrect fault isolation, missed fault detection, and false alarm. The proposed diagnostic scheme proves to have good performance also out of the domain on which it was trained.<<ETX>>

A digital signal-processing instrument for impedance measurement

A digital signal processing (DSP)-based instrument for impedance measurement is proposed. It implements two measurement techniques: the first technique allows the tracking of a time varying impedance to be carried out, and the second assures that good accuracy is obtained in reasonable measurement times. Both the software and the hardware of a prototype model are described in detail. The measurement rates and accuracy are evaluated and compared with those obtained using a reference Digibridge for a wide range of impedance values.

An advanced neural-network-based instrument fault detection and isolation scheme

An advanced scheme for instrument fault detection and isolation is proposed. It is based on artificial neural networks (ANNs), organized in layers and handled by knowledge-based analytical redundancy relationships. ANN design and training is performed by genetic algorithms which allow ANN architecture and parameters to be easily optimized. The diagnostic performance of the proposed scheme is evaluated with reference to a measurement station for automatic testing of induction motors.

An enhanced fiber optic temperature sensor system for power transformer monitoring

The paper deals with a sensor for temperature measurements in fluids. The sensor is based on a fiber-optic sensing element and on microcontroller-based signal processing hardware. The physical principle behind its operation is briefly reviewed, and its application to power transformer hot-spot temperature measurement is reported. Laboratory experimental results are given for both sensor characterization and measurements carried out on a 25-kVA oil-cooled power transformer.