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Since its introduction at INPE, in the late 1960s, Systems Engineering has passed through phases of greater and lesser importance. Three different phases are clearly recognized. The first two phases are closely associated with efforts to develop space systems. The third and present phase is associated to the recent growth in the importance of Systems Engineering to the development of the contemporary large and complex systems. This paper is a summary review of the history of Systems Engineering at INPE from its inception to the present.

Systems are becoming increasingly more complex. To follow this increasingly complexity, systems engineering must evolve rapidly with the introduction of new methodologies, processes, tools, etc. Due to this rapid evolution, little attention is dedicated to the study of the history of its evolution. Currently there is the initiative of installation of a chapter of INCOSE (International Council on Systems Engineering) in Brazil and from this initiative emerged the interest of recovering the history of systems engineering in the country. There are indications that the introduction of systems engineering into Brazil occurred in the late 1960's, directly from NASA and that its first applications in Brazil were in Space Systems Engineering. This paper recovers the origins of systems engineering, of its introduction into Brazil, and of its use in space systems engineering.

In this work we discuss the fault avoidance and the fault tolerance approaches for increasing the reliability of aerospace and automotive systems. This includes: the basic definitions/concepts (reliability, maintainability, availability, redundancy, etc.), and characteristics (a priori analysis, a posteriori analysis, physical/hardware redundancy, analytical/software redundancy, etc.) of both approaches, their mathematical background and models (exponential, Weilbull, etc.), their basic theory, their methods and techniques (fault trees, dependence diagrams, Markov chains, etc.), some of their standards (SAE-ARP4761, AC 25.1309, etc.) and simulation environments (Cafta, etc.), and their applications to the reliability analysis and reliability improvement of aerospace and automotive vehicles. This is illustrated by some examples driven from the aerospace and automotive industries.

This paper presents the preliminary results of an "a posteriori" exercise of application of a Requirements Traceability Automation Tool (RT tool) to a set of documents. The documents have been prepared according to established Space System Engineering methodologies and with attention to text quality, but without attention to requirements traceability because the processes and methodologies used during their preparation predates the emergence of the processes and methodologies developed by Requirements Engineering (RE). This study is intended to determine some of the benefits of using a RT tool when compared with the previously used processes and methodologies. The set of documents under scrutiny have been prepared in the frame of the development of the CBERS-3 satellite (China-Brazil Earth Resources Satellite) and is composed of system, subsystem and equipment specification and covering documents related to the Electrical Power Subsystem (EPS) of the satellite.

This paper presents some techniques for fault diagnosis in aerospace and automotive systems. A diagnosis technique is an algorithm to detect and isolate fault components in a dynamic process, such as sensor biases, actuator malfunctions, leaks and equipment deterioration. Fault diagnosis is the first step to achieve fault tolerance, but the redundancy has to be included in the system. This redundancy can be either by hardware or software. In situations in which it is not possible to use hardware redundancy only the analytical redundancy approach can be used to design fault diagnosis systems. Methods based on analytical redundancy need no extra hardware, since they are based on mathematical models of the system.

On several engineering applications high Reliability is one of the most wanted features. The aspects of Reliability play a key role in design projects of aircraft, spacecraft, automotive, medical, bank systems, and so, avoiding loss of life, property, or costly recalls. The highly reliable systems are designed to work continuously, even upon external threats and internal Failures. Very convenient is the fact that the term 'Failure' may have its meaning tailored to the context of interesting, as its general definition refers to it as "any deviation from the specified behavior of a system". The above-mentioned 'deviation' may refer to: performance degradation, operational misbehavior, deviation of environmental qualification levels, Safety hazards, etc. Nevertheless, Reliability is not the only requirement for a modern system. Other features as Availability, Integrity, Security and Safety are always part of the same technical specification, in a same level of importance.

Life-critical aeronautical projects are increasing their lifecycle up to the point where a product is developed to be available in the market for more than 70 years, but requiring daily service support and replacement parts. Therefore, unavailability of components or services can have a severe impact over the product through its lifecycle. In this work we study some causes of unavailability of components and services and their effects over the lifecycle of an aeronautical project, to propose recommendations, alternatives and actions to be taken in the early phases of project development to help mitigate such effects over the product lifecycle. To do that, we initially present the causes of unavailability of components and services. Then, we discuss some of their effects over the lifecycle of an aeronautical project.

In this work we discuss some types of simulators and simulations, their characteristics and applications to the simulation and control of aerospace vehicles. This includes: the basic definitions, types and characteristics of simulators and simulations (physical, computational, hybrid, etc.; discrete events, discrete time, continuous time, etc; deterministic, stochastic, etc.) their basic compromise (simplicity × fidelity), their man-machine interfaces and interactions (virtual, constructive, live, etc.), their evolution law (time, events, mixed, etc.), their architectures (“standalone”, PIL, HIL, MIL, DIS, HLA, etc.), their environments (discrete, continuous, hybrid, etc.) and their applications to the simulation and control of aerospace vehicles. This is illustrated by some examples driven from the aerospace industry

In this work we discuss some types of simulation environments and laboratories, their characteristics and applications to the simulation and control of aerospace vehicles. This includes: the basic definitions, types and characteristics of simulators and simulations (physical, computational, hybrid, etc.; discrete events, discrete time, continuous time, etc; deterministic, stochastic, etc.) their basic compromise (simplicity × fidelity), their man-machine interfaces and interactions (virtual, constructive, live, etc.), their evolution law (time, events, mixed, etc.), their architectures (“stand-alone”, PIL, HIL, MIL, DIS, HLA, etc.), and especially, their environments (discrete, continuous, hybrid, etc.) and laboratories (physical, computational, hybrid, etc.), and their applications to the simulation and control of aerospace vehicles. This is illustrated by some examples driven from the aerospace industry.

In this work we discuss some types of simulation architectures and standards, their characteristics and applications to the simulation and control of aerospace vehicles. This includes: the basic definitions, types and characteristics of simulators and simulations (physical, computational, hybrid, etc.; discrete events, discrete time, continuous time, etc; deterministic, stochastic, etc.) their basic compromise (simplicity x fidelity), their man-machine interfaces and interactions (virtual, constructive, live, etc.), their evolution law (time, events, mixed, etc.), their architectures (“stand-alone”, PIL, HIL, MIL, DIS, HLA, etc.), their standards (OMBA, SIMNET, ALSP, DIS, HLA 1.3, HLA 1516, ASIA, AP2633, etc.) and their applications to the simulation and control of aerospace vehicles. This is illustrated by some examples driven from the aerospace industry

Several papers presents fault detection and isolation techniques for fault in only one sensor; in this paper we will present a technique for multiples faults detection and isolation in sensors of dynamic systems. Multiples faults have less probability to occur but it is not null. So in critical applications the system needs to be operational even in this situation. In this paper we will present a design for a Multiples Faults Detection and Isolation (MFDI) system, an example to illustrate this technique and its respective results.

Modeling and Simulation (M&S) of dynamic systems based on computers is a multidisciplinary field that involves several knowledge areas and tools, and is broadly used in all development areas of space industry such as rocket and satellite design and construction. Once space systems are divided into several subsystems for ease of engineering, their models are divided the same way for the same reason. Such models may be done using different computational tools that are based on either physical flows, informational flows, or hybrid flows, depending on the subsystem nature. This is specially true for a satellite propulsion subsystem, and its physical (volume, mass, energy, enthalpy, entropy, linear momentum, etc.) flows. This paper presents the modeling and simulation of a satellite propulsion subsystem by physical and signal flows. To accomplish this task, two different computational tools were used: AMESim and MatLab.

Control systems that can switch between control or plant modes have the advantage of being simpler to design than an equivalent system with a single mode. However, the transition between these modes can introduce steps or overshootings in the state variables, and this can degrade the performance or even damage the system. This is can be of extreme importance in fields such as aerospace and automobilistic, as the switching between manual and autopilot modes or the switching of gears In this work, we will use integral criteria in original ways, to determine a coefficient on the system which should optimize the trajectory of the control signal, during the switching between two modes. Effectively, each transition will be done by a subsystem specific for it, according to the selected criterion. The simulations will be made in MATRIXx, MatLab or both, using models chosen from aerospace or automobilistic fields.

This works presents the generation and customization of real time code for embedded controllers using a modeling and simulation environment. When the controller model is considered satisfactory, the developers can use a code generation tool to build a real time source code capable to be migrated to an embedded target processor. The code generation tool used is capable to generate real time code in ANSI C or ADA 95 languages. This process can be customized to adequate to a target processor and/or a Real Time Operating System (RTOS). The code customization can be achieved using a specific Template Programming Language (TPL) that specifies how the code will be generated. This technique makes it possible the instantiation of real time embedded controllers code using the same controller model to a wide variety of target processors and/or RTOSs.

The purpose of this work is Fault Detection and Diagnosis (FDD) on a Knock Sensor because some of the modern petrol engines operate on the efficient four-stroke cycle, where each cylinder of the engine contains an intake and exhaust poppet valve that is operated at the appropriate time. The ECM (Engine Control Module) uses the Knock Sensor signal to control timing. The Knock Sensor detects engine knock and sends voltage signal to the ECM. These signals can be sufficient to detect abnormal combustion, like ‘spark knock’ and ‘surface ignition’. Engine knock occurs within a specified range. The Knock Sensor, located in the engine block, cylinder head, or intake manifold is tuned to detect that frequency, which motivates the use of signal models for detection. But this sensor is a wide-band accelerometer of the piezoelectric type too. Analogy with a general seismic mass system is possible since it is a general damped second order vibrating system which is forced into oscillatory motion.

Eigenstructure techniques allow to detect and isolate faulty components in a dynamic process, such as sensor biases, actuator malfunctions, changes in dynamic parameters due to leaks and deterioration. Fault detection is the first step to achieve fault tolerance, but for this the redundancy has to be included in the system. This redundancy can be either by hardware or by software. In situations in which it is not possible to use hardware redundancy only the software redundancy can be used. Therefore using eigenstructure techniques, for the fault detection and isolation, the tests can be done through the angle between the residue vector direction and the fault direction vector. By this way, we can reduce false alarm and the alarm loss rates due to the noise and changes in system parameters.

In this work we discuss current trends driving the aerospace and automotive systems architectures. This includes trends as: 1) pos-globalization and regionalization; 2) the formation of knowledge oligopolies; 3) commonality, standardization and even synergy (of components, tools, development process, certification agents, standards); 4) reuse and scalability; 5) synergy of knowledge and tools convergence; 6) time, cost and quality pressures and innovation speed; 7) environmental and safety issues; and 8) abundance of new technologies versus scarcity of skilled manpower to apply them.

Internet of Things (IoT) for real-time applications are demanding more and more high performance, precision, accuracy, modularity, integration, dependability and other attributes in a complex and/or highly integrated environment. Such systems need to provide coordination among the integrated components (e.g. sensors, computer, controller and networks) for enabling the application to take real-time measurements and to translate into controllable, observable and smart actions with strict timing requirements. Therefore, coordination and synchronization are required to ensure the controllable, observable and smart actions of real-time IoT systems. This paper shows the design issues about the coordination and synchronization in the internet of things applied to real-time applications. We also show the current coordination and synchronization techniques and their design issues when applied to IoT systems.

Many control systems switch between control modes according to necessity. That is often simpler than designing a full control to all situations. However, this creates new problems, as determining the composed system stability and the transient during switching. The latter, while temporary, may introduce overshooting that degrade performance and damage the plant. This is particularly true for the MultiMission Platform (MMP), a generic service module currently under design at INPE. Its control system can be switched among nine main Modes of Operation and other submodes, according to ground command or information coming from the control system, mainly alarms. It can acquire one and three axis stabilization in generic attitudes, with actuators including magnetotorquers, thrusters and reaction wheels.

Systems such as satellites, airplanes, cars and air traffic controls are becoming more complex and/or highly integrated. These systems integrate several technologies inside themselves, and must be able to work in very demanding environments, sometimes with few or none maintenance services due to their severe conditions of work. To survive such severe work conditions, the systems must present high levels of reliability, which are achieved through different approaches, processes, etc. These unfold in many: levels of aggregation (systems, subsystems, equipments, components, etc.), phases of their lifecycles (conception, design, manufacturing, assembly, integration, tests, operation, etc.), environments (land, sea, air, space, etc.), types of components/applications/experiences/technological communities (nuclear, aerospace, military, automotive, medical, commercial, etc.), leaded by the widespread use of semiconductors.