Giovanni Betta

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.

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.

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>>

On-line fault detection and diagnosis obtained by implementing neural algorithms on a digital signal processor

A measurement instrument for on-line fault detection and diagnosis is proposed. It is based on the implementation of a neural network algorithm on a processor specialized in digital signal processing and provided with suitable data acquisition and generation units. Two specific implementations are detailed. The former uses the neural-network to simulate on-line the correct system behavior, thus allowing the fault detection to be achieved by comparing the neural network output with the measured one. The latter uses the neural network to classify on-line the system as correct or faulty, thus allowing the fault detection and diagnosis to be achieved simultaneously. These two implementations are applied to detect on-line and diagnose faults on a real system in order to point out different fields of application and to highlight the performance of the measurement apparatus.

Crack Depth Estimation by Using a Multi-Frequency ECT Method

In many industrial application fields as manufacturing, quality control, and so on, it is very important to highlight, to locate, and to characterize the presence of thin defects (cracks) in conductive materials. The characterization phase tries to determine the geometrical characteristics of the thin defect namely the length, the width, the height, and the depth. The analysis of these characteristics allows the user in accepting or discarding realized components and in tuning and improving the production chain. The authors have engaged this line of research with particular reference to non-destructive testing applied to the conductive material through the use of eddy currents. They realized methods and instruments able to detect, locate, and characterize thin defects. In this paper, a novel measurement method able to improve the characterization of the crack depth is proposed. It is based on the use of a suitable multi-frequency excitation signals and of digital signal processing algorithms. Tests carried out in an emulation environment have shown the applicability of the method and have allowed the tuning of the measurement algorithm. Tests carried out in a real environment confirm the goodness of the proposal.

GMR-Based ECT Instrument for Detection and Characterization of Crack on a Planar Specimen: A Hand-Held Solution

This paper proposes a novel instrument for eddy-current (EC) nondestructive testing on conductive materials. The instrument is composed by a smart EC probe, based on a GMR sensor, and a suitable processing unit. The key features of the proposed instrument are the capability of detecting, locating, and characterizing thin defects as superficial and subsuperficial cracks. The main goal of the proposal is the realization of a very low cost instrument that is able to reach performance comparable with instrumentation available on the market and especially suited for application fields such as aerospace and maritime. In this paper, the steps followed for the instrument realization, together with its experimental characterization, are described in detail.

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.

A measurement system based on magnetic sensors for nondestructive testing

The paper deals with a measurement system based on a low-cost eddy current probe for nondestructive testing (NDT) on conducting materials aimed at reconstructing the shape and position of thin cracks. The magnetic probe is characterized, highlighting good repeatability, linearity, and overall accuracy. A number of different measurement approaches are investigated, in order to choose the most appropriate for NDT applications. A numerical method is then illustrated; it proves to be able to reconstruct cracks starting from noisy measurement data.