Masking failures of multidimensional sensors (extended abstract)

When a computer monitors a physical process, the computer uses sensors to determine the values of the physical variables that represent the state of the process. A sensor can sometimes fail, however, and in the worst case report a value completely unrelated to the true physical value. The work described is motivated by a methodology for transforming a process control program that can not tolerate sensor failure into one that can. In this methodology, a reliable abstract sensor is created by combining information from several real sensors that measure the same physical value. To be useful, an abstract sensor must deliver reasonably accurate information at reasonable computational cost. Sensors are considered that deliver multidimensional values (e.g., location or velocity in three dimensions, or both temperature and pressure). Geometric techniques are used to derive upper bounds on abstract sensor accuracy and to develop efficient algorithms for implementing abstract sensors

Advanced information processing system for advanced launch system: Avionics architecture synthesis

The Advanced Information Processing System (AIPS) is a fault-tolerant distributed computer system architecture that was developed to meet the real time computational needs of advanced aerospace vehicles. One such vehicle is the Advanced Launch System (ALS) being developed jointly by NASA and the Department of Defense to launch heavy payloads into low earth orbit at one tenth the cost (per pound of payload) of the current launch vehicles. An avionics architecture that utilizes the AIPS hardware and software building blocks was synthesized for ALS. The AIPS for ALS architecture synthesis process starting with the ALS mission requirements and ending with an analysis of the candidate ALS avionics architecture is described

Robust Sensor Fusion Algorithms: Calibration and Cost Minimization.

A system reacting to its environment requires sensor input to model the environment. Unfortunately, sensors are electromechanical devices subject to physical limitations. It is challenging for a system to robustly evaluate sensor data which is of questionable accuracy and dependability. Sensor fusion addresses this problem by taking inputs from several sensors and merging the individual sensor readings into a single logical reading. The use of heterogeneous physical sensors allows a logical sensor to be less sensitive to the limitations of any single sensor technology, and the use of multiple identical sensors allows the system to tolerate failures of some of its component physical sensors. These are examples of fault masking, or N-modular redundancy. This research resolves two problems of fault masking systems: the automatic calibration of systems which return partially redundant image data is problematic, and the cost incurred by installing redundant system components can be prohibitive. Both are presented in mathematical terms as optimization problems. To combine inputs from multiple independent sensors, readings must be registered to a common coordinate system. This problem is complex when functions equating the readings are not known a priori. It is even more difficult in the case of sensor readings, where data contains noise and may have a sizable periodic component. A practical method must find a near optimal answer in the presence of large amounts of noise. The first part of this research derives a computational scheme capable of registering partially overlapping noisy sensor readings. Another problem with redundant systems is the cost incurred by redundancy. The trade-off between reliability and system cost is most evident in fault-tolerant systems. Given several component types with known dependability statistics, it is possible to determine the combinations of components which fulfill dependability constraints by modeling the system using Markov chains. When unit costs are known, it is desirable to use low cost combinations of components to fulfill the reliability constraints. The second part of this dissertation develops a methodology for designing sensor systems, with redundant components, which satisfy dependability constraints at near minimal cost. Open problems are also listed

Towards a cloud‑based automated surveillance system using wireless technologies

Cloud Computing can bring multiple benefits for Smart Cities. It permits the easy creation of centralized knowledge bases, thus straightforwardly enabling that multiple embedded systems (such as sensor or control devices) can have a collaborative, shared intelligence. In addition to this, thanks to its vast computing power, complex tasks can be done over low-spec devices just by offloading computation to the cloud, with the additional advantage of saving energy. In this work, cloud’s capabilities are exploited to implement and test a cloud-based surveillance system. Using a shared, 3D symbolic world model, different devices have a complete knowledge of all the elements, people and intruders in a certain open area or inside a building. The implementation of a volumetric, 3D, object-oriented, cloud-based world model (including semantic information) is novel as far as we know. Very simple devices (orange Pi) can send RGBD streams (using kinect cameras) to the cloud, where all the processing is distributed and done thanks to its inherent scalability. A proof-of-concept experiment is done in this paper in a testing lab with multiple cameras connected to the cloud with 802.11ac wireless technology. Our results show that this kind of surveillance system is possible currently, and that trends indicate that it can be improved at a short term to produce high performance vigilance system using low-speed devices. In addition, this proof-of-concept claims that many interesting opportunities and challenges arise, for example, when mobile watch robots and fixed cameras would act as a team for carrying out complex collaborative surveillance strategies.Ministerio de Economía y Competitividad TEC2016-77785-PJunta de Andalucía P12-TIC-130

Diagnosing mechanical damages not detected by the OBD system of diesel engines

The paper presents the possibilities of diagnosing with the use of vibracoustic signal of mechanical damages of diesel engines, which are not detected by the OBD system. This system does not recognize such damages, but it masks them delaying information about the damage to the point where the regulatory values of controlling work of the engine will exceed the limit values. This is because the aim of the OBD system is to ensure the minimization of exhaust emissions. There were made measurements of vibroacoustic signal of the head of the car engine with fuel injection system Common Rail, where there were introduced mechanical failures typical and dangerous for the engine. It has been proved that the OBD system does not recognize these damages and the work of the engine is correct. Therefore, there is a need for an additional diagnostic system which supports the OBD system in detection of this type of damages. It seems that the use of vibroacoustic signal as an additional analyzed signal should improve the sensitivity of the on-board diagnostic system to damages of diesel engine. The author tested the sensitivity of vibroacoustic signal to damages introduced to the engine and he presented the proposition of the system based on simple measures of the vibroacoustic signal, which allows for both detecting mechanical damages and their identification

Resilient Multidimensional Sensor Fusion Using Measurement History

This work considers the problem of performing resilient sensor fusion using past sensor measurements. In particular, we consider a system with n sensors measuring the same physical variable where some sensors might be attacked or faulty. We consider a setup in which each sensor provides the controller with a set of possible values for the true value. Here, more precise sensors provide smaller sets. Since a lot of modern sensors provide multidimensional measurements (e.g., position in three dimensions), the sets considered in this work are multidimensional polyhedra. Given the assumption that some sensors can be attacked or faulty, the paper provides a sensor fusion algorithm that obtains a fusion polyhedron which is guaranteed to contain the true value and is minimal in size. A bound on the volume of the fusion polyhedron is also proved based on the number of faulty or attacked sensors. In addition, we incorporate system dynamics in order to utilize past measurements and further reduce the size of the fusion polyhedron. We describe several ways of mapping previous measurements to current time and compare them, under di erent assumptions, using the volume of the fusion polyhedron. Finally, we illustrate the implementation of the best of these methods and show its e ectiveness using a case study with sensor values from a real robot

Building and Commissioning of the CMS Pixel Barrel Detector

The CMS pixel barrel detector is a complex system that consists of 768 segmented silicon sensor modules. The total number of readout channels in the system is about 48 million. An overview on the module assembly and qualification procedures as well as testing results will be presented. The assembly of the detector control and readout electronics on the supply tube, the integration of the final system and the installation into CMS will be explained. The strategy and results from the early commissioning of the complete system that includes the performance of the hardware and the data acquisition and control software will be reviewed