Exploring the impact of different cost heuristics in the allocation of safety integrity levels

Contemporary safety standards prescribe processes in which system safety requirements, captured early and expressed in the form of Safety Integrity Levels (SILs), are iteratively allocated to architectural elements. Different SILs reflect different requirements stringencies and consequently different development costs. Therefore, the allocation of safety requirements is not a simple problem of applying an allocation "algebra" as treated by most standards; it is a complex optimisation problem, one of finding a strategy that minimises cost whilst meeting safety requirements. One difficulty is the lack of a commonly agreed heuristic for how costs increase between SILs. In this paper, we define this important problem; then we take the example of an automotive system and using an automated approach show that different cost heuristics lead to different optimal SIL allocations. Without automation it would have been impossible to explore the vast space of allocations and to discuss the subtleties involved in this problem

Inversion-Based Approach for Detection and Isolation of Faults in Switched Linear Systems

This paper addresses the problem of the left inversion of switched linear systems from a diagnostics perspective. The problem of left inversion is to reconstruct the input of a system with the knowledge of its output, whose differentiation is usually required. In the case of this work, the objective is to reconstruct the system’s unknown inputs, based on the knowledge of its outputs, switching sequence and known inputs. With the inverse model of the switched linear system, a real-time Fault Detection and Isolation (FDI) algorithm with an integrated Fuzzy Logic System (FLS) that is capable of detecting and isolating abrupt faults occurring in the system is developed. In order to attenuate the effects of unknown disturbances and noise at the output of the inverse model, a smoothing strategy is also used. The results are illustrated with an example. The performance of the method is validated experimentally in a dc-dc boost converter, using a low-cost microcontroller, without any additional components.This work was funded by FCT—Fundação para a Ciência e a Tecnologia, within the project SAICTPAC/0004/2015—POCI-01-0145-FEDER-016434.info:eu-repo/semantics/publishedVersio

Experimental evaluation of new one-chip solution for induction motor drives

The design of high performance induction motor drives is a complex task, and the every day compelling requirements in energy efficiency and performance assumes the motivation on finding a more integrated solution on implementing induction motor control. The main subjects of this paper are two: to discuss the IFOC one-chip solution and to explore the development of simple graphical applications in order to operate this control in a simple and effective way. Experimental results are presented to illustrate the main points of our paper

A low cost solution for laboratory experiments in induction motor control

In this paper we present a controller suitable for educational activities in electric drives. A prototype has been designed specifically to meet the requirement of low cost and it contains all of the active functions required to implement the open loop control of an induction motor. In this way, the prototype allows the easy assimilation of important concepts and enables the understanding of the enclosed subsystems. Some experiments that highlight the quality of the proposed approach are presented

A boot-strap estimator for joint flux and parameters online identification for vector controlled induction motor drives

This paper presents a new approach for joint rotor flux and electrical parameters on-line identification in vector controlled high-performance induction motor drives based on a boot-strap estimator that uses a reduced order extended Kalman filter for rotor flux components and rotor parameters estimation and a recursive prediction error method for stator parameters estimation. Within the prediction error method some approaches are used and compared that affect both the adaptation gain and the direction in which the updates of stator parameters are made. The induction motor model structures are described in the rotor reference frame in order to reduce the computational effort by using a higher sampling time interval

Flux and parameters identification of vector-controlled induction motor in the rotor reference frame

This paper presents a new approach for the simultaneous identification of rotor flux components in the rotor reference frame and electrical parameters of a vector controlled induction motor, for real-time implementations, using an extended Kalman filter (EKF) and a reduced order model structure for lower computational effort. The proposed new method requires the measurement of motor speed, stator voltages and currents signals. Using a motor model structure with four electrical parameters, the estimation of flux space phasor and rotor parameters is presented. The estimation is subsequently further extended to include the motor stator parameters and the results are analyzed as well as robustness. Simulated and experimental studies highlight the improvements brought by this new approach, mainly, a simple and reduced state equation, the introduced scalar output equation and lower computational cadency, by using lower sampling frequencies in the proposed rotor reference frame

Indirect parameter estimation of continuous-time systems using discrete time data

This paper addresses the problem of parameter estimation of continuous-time systems using samples of its input-output data. We propose a method based on the bilinear transformation to obtain an equivalent discrete-time model. Introducing a new polynomial pre-filter it .is possible to compute the physical parameters via inverse mapping between the discrete-time and the continuous-time models. A simulation example is given to illustrate the noise effects in the parameter estimation results. Using experimental results, we demonstrate the ability of the estimator. to handle real measurement problems

Classes of model structures for state and parameter identification of vector controlled induction machines

The purpose of this paper is to present a synthesis of classes of model structures for joint state and parameter identification of vector controlled induction motors for real time and normal operating conditions. Based on its classical model a set of new classes of model structures is discussed and proposed for simultaneous estimation of rotor flux components and electrical parameters

Modelling and simulation of power electronic systems using a bond graph formalism

This paper deals with the modelling of power electronic systems using the bond graph formalism. The switching components are modelled using an ideal representation so that a constant topology system is obtained. The purpose of the present contribution is to discuss a technique that combines bond graph energy-flow modelling and signal-flow modelling schemes for simulation and prototyping of signal processing algorithms in power electronics systems. In this paper, we will discuss models of the use of fully-controlled, semi-controlled and non-controlled switches in the field of power static converters. By concept, a simulation environment can be examined at different abstraction or hierarchy levels. The approach in this paper is, accordingly, the formulation of a simulation task at different levels: component level, topology level, functional description and implementation description. The paper concludes with two practical examples of simulation of power electronics systems

A new online identification methodology for flux and parameters estimation of vector controlled induction motors

A new online identification methodology for estimation of the rotor flux components and the main electrical parameters of vector controlled induction motors is presented in this paper. The induction motor model is referred to the rotor reference frame for estimation of rotor flux and rotor parameters, and referred to the stator reference frame to estimate stator parameters. The stator parameters estimation is achieved by a prediction error method based on a model structure described by a linear regression that is independent of rotor speed and rotor parameters. The rotor flux components and rotor parameters are estimated by a reduced order extended Kalman filter, using a 4th-order state-space model structure where the state equation is described by matrices that are diagonal and independent of rotor speed as well as stator parameters. Both methods work in a boot-strap manner