Dependability considerations of redundant sensor systems

Abstract Integrated sensor systems, which are produced in high volume for safety-critical applications such as automotive, require careful design and optimization strategies. To increase their reliability, such systems are often used in redundant configurations. However, this is in many cases not sufficient: requirements of high production yield, reliability in operation, low probabilities for non-detected system faults, in combination with fast development speed and low production costs require more detailed and careful design approaches. We consequently suggest statistical design, estimation and optimization approaches for efficient product definition and design. This methodology is summarized in a general way for redundant sensor systems. In a first step, the individual sensing channel performance is optimized statistically. In a second step, the dependability figures are optimized dependent on the redundant sensor output function and its diagnostic mechanism parameters based on statistical considerations of faults. The suggested methodology is shown exemplary for a redundant integrated linear Hall magnetic field sensor system for safety-critical automotive applications.

[1]  Christine Barthod,et al.  An overall methodology for reliability prediction of mechatronic systems design with industrial application , 2016, Reliab. Eng. Syst. Saf..

[2]  Aarnout Brombacher,et al.  Using a failure modes, effects and diagnostic analysis (FMEDA) to measure diagnostic coverage in programmable electronic systems , 1999 .

[3]  Dirk Hammerschmidt,et al.  Redundant and Diverse Magnetic Field Digital Linear Hall Sensor Concept for ASIL D Applications , 2017 .

[4]  Yunhai Hou.  A new international standard: Combining safety with dependability , 2011, The Proceedings of 2011 9th International Conference on Reliability, Maintainability and Safety.

[5]  Paolo Maggiore,et al.  Failure rate evaluation method for HW architecture derived from functional safety standards (ISO 19014, ISO 25119, IEC 61508) , 2017, Reliab. Eng. Syst. Saf..

[6]  Linus Maurer,et al.  Towards simulation based evaluation of safety goal violations in automotive systems , 2014, FDL 2014.

[7]  Stefan Rigert,et al.  Integrated hall-based magnetic platform for position sensing , 2017, ESSCIRC 2017 - 43rd IEEE European Solid State Circuits Conference.

[8]  Hubert Zangl,et al.  Optimal Design of Multiparameter Multisensor Systems , 2008, IEEE Transactions on Instrumentation and Measurement.

[9]  Costas J. Spanos,et al.  Non-Gaussian uncertainty propagation in statistical circuit simulation , 2011, 2011 12th International Symposium on Quality Electronic Design.

[10]  Hubert Zangl,et al.  Diagnostic coverage estimation method for optimization of redundant sensor systems , 2017, 2017 IEEE SENSORS.

[11]  Hubert Zangl,et al.  Optimal design of angular position sensors , 2017 .

[12]  Marvin Rausand,et al.  Proof-testing strategies induced by dangerous detected failures of safety-instrumented systems , 2016, Reliab. Eng. Syst. Saf..

[13]  Hubert Zangl,et al.  Robust design of a 3D- and inkjet-printed capacitive force/pressure sensor , 2016, 2016 17th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE).

[14]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[15]  Hubert Zangl,et al.  Robust design of an inkjet-printed capacitive sensor for position tracking of a MOEMS-mirror in a Michelson interferometer setup , 2017, Microtechnologies.

[16]  KyuBong Yeon,et al.  Fault detection and diagnostic coverage for the domain control units of vehicle E/E systems on functional safety , 2017, 2017 20th International Conference on Electrical Machines and Systems (ICEMS).

[17]  Yvon Savaria,et al.  Dependability modeling and optimization of triple modular redundancy partitioning for SRAM-based FPGAs , 2019, Reliab. Eng. Syst. Saf..

[18]  Hubert Zangl,et al.  Statistical Modeling of Integrated Sensors for Automotive Applications , 2018, 2018 International Conference of Electrical and Electronic Technologies for Automotive.

[19]  Mario Motz,et al.  Continuous-Time ∆Σ-Converters and Stress Compensation Capability , 2006 .

[20]  Steven E. Rigdon,et al.  Model-Oriented Design of Experiments , 1997, Technometrics.

[21]  Marcantonio Catelani,et al.  Failure modes, mechanisms and effect analysis on temperature redundant sensor stage , 2018, Reliab. Eng. Syst. Saf..

[22]  Hans G. Kerkhoff,et al.  A dependable AMR sensor system for automotive applications , 2017, 2017 International Test Conference in Asia (ITC-Asia).

[23]  I. S. Belkhodja,et al.  Speed/position sensor fault tolerant control in adjustable speed drives - A review. , 2016, ISA transactions.

[24]  Ping He,et al.  A comprehensive survey on the reliability of mobile wireless sensor networks: Taxonomy, challenges, and future directions , 2018, Inf. Fusion.

[25]  Mario Motz,et al.  Electrical Compensation of Mechanical Stress Drift in Precision Analog Circuits , 2017 .

[26]  Luca Giorgi.  Sensitivity calibration and test of a 3D hall integrated sensor device with an external magnetic field source on a new ATE concept , 2015, 2015 IEEE 20th International Mixed-Signals Testing Workshop (IMSTW).

[27]  Colin C. McAndrew.  Statistical Modeling Using Backward Propagation of Variance (BPV) , 2010 .

[28]  B. Schaffer,et al.  An Integrated Magnetic Sensor with Two Continuous-Time /spl Delta//spl Sigma/-Converters and Stress Compensation Capability , 2006, 2006 IEEE International Solid State Circuits Conference - Digest of Technical Papers.

[29]  X. Xia,et al.  Monolithic Integration of Pressure Plus Acceleration Composite TPMS Sensors With a Single-Sided Micromachining Technology , 2012, Journal of Microelectromechanical Systems.

[30]  Uwe Kiencke,et al.  Duo duplex drive-by-wire computer system , 2005, Reliab. Eng. Syst. Saf..

[31]  Anthony N. Pettitt,et al.  A Review of Modern Computational Algorithms for Bayesian Optimal Design , 2016 .