Analytically Redundancy Based IFDI Scheme for Semi-Active Suspension Systems in Motorcycle

Transcription

1 Journal of Physics: Conference Series PAPER OPEN ACCESS Analytically Redundancy Based IFDI Scheme for Semi-Active Suspension Systems in Motorcycle To cite this article: D Capriglione et al 2018 J. Phys.: Conf. Ser View the article online for updates and enhancements. This content was downloaded from IP address on 21/11/2018 at 00:13

2 Analytically Redundancy Based IFDI Scheme for Semi-Active Suspension Systems in Motorcycle D Capriglione, M Carratù, A Pietrosanto, P Sommella Dept. of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, Fisciano (SA), Italy dcapriglione@unisa.it Abstract. Following the current trend of the automotive context, also in the motorcycle applications the employment of sensors for improving vehicle performance and passenger s safety are becoming even more widespread. Among the most recent examples of this trend we can find the systems designed to electronically control and to on-line adapt the suspension subsystems. Of course, the correct operating of such systems is strictly related to the reliability of the information provided by the sensors involved in the related measurement and control chain. In this framework, the paper proposes an Instrument Fault Detection and Isolation (IFDI) Scheme for a motorcycle semi-active suspension system equipping a real prototype motorcycle. In particular, the proposed scheme is based on the analytical redundancy existing among the quantities measured by the involved sensors. As for the residuals generation, suitable soft sensors based on artificial neural networks are proposed to allow the fault detection and isolation. 1. Introduction The topic of the Intelligent Sensors and Instruments has gained a huge success in the past ten years, especially with the development of neural networks, fuzzy logic, and soft computing algorithms. Intelligence is, in fact, a human characteristic usually also attributed to machines and devices showing the capability to offer advanced functional services in many operating modes all along their life cycle (installation, initialization, exploitation, maintenance modes). Thus, automatic measurement system can be defined as intelligent if only it exhibits Instrument Fault Detection and Isolation (IFDI) features [1]. The corresponding theory is fundamental in all those fields where the reliability of data measured by sensors has to be assured before using them for implementing the subsequent features as it happens in plant (or building) monitoring systems, On-line Diagnostic Systems, Closed-Loop Systems for the automatic control of machines or processes. Indeed, in all these cases, Instrument faults not detected and isolated may cause false or missing actions (alarms, turning-on and off of devices) with potentially damages and safety issues on plants, systems and end-users involved. IFDI theory is a research field expanding, because physical redundancy based solutions (which require double or triple the number of identical sensors for each measurand) are always more often integrated or even substituted by model-based redundancy approaches, thus reducing costs, dimension and reliability. The interest on sensors fault isolation, exploiting the synergy of functional and physical redundancy, is no more limited to examples of safety critical systems such as Nuclear Power Plant [2] or avionic system [3]. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by Ltd 1

3 Sensor Type Model Manufacturer Symbol Linear S1 Displacement SLS130 Penny & Giles Sensor S2 Gyroscope L3GD20 ST Microelectronics S3 Magnetic Encoder Dorman S4 Figure 1. The system under test Table 1. Main sensors for measuring the vertical dynamics of the test motorcycle On the contrary, in such fields as Automotive [4] and Industry 4.0 [4] the high dynamics systems and on-line applications can be pointed out as one of the most interesting research focus. As an example, in the context of motorcycle, we can assist to an increasing interest toward semi-active suspension control systems able to improve both the comfort and the passenger s safety in both racing and original equipment manufacturer applications. Such systems implement suitable strategies based on the measurement from several sensors (including accelerometers, stroke sensors, gyroscope and magnetic encoders) with the aim of regulating in real-time the damping forces. Based on previous experience about the adoption of the analytical redundancy for IFDI application [6], the authors developed in [7] a soft sensor based on NARX Neural Network for predicting the rear suspension stroke of two-wheeled vehicle whereas, in [8]-[9] the corresponding Fault Detection scheme was designed and verified through the successful implementation on a microcontroller STM32 based on the general-purpose ARM-M4 architecture. In the following, after a brief recall of the system under test, the methodological approach is described for developing a complete IFDI scheme concerning with all the sensors involved in the suspension control system for motorcycle. 2. The System under Test The test bed for the research activity is composed by a motorcycle SUZUKI GSX-1000 (schemed in Figure 1), equipped with the sensors (see Table 1) needed to implement the control strategy of the semi-active suspension system. The data acquisition (at sampling frequency equal to 1 khz) from the sensors is guaranteed by the motorcycle CAN Bus network operating at 1 Mbps and a Data Logger provided with 4-analog inputs and 12 bit-adc. In order to design, tune and validate the IFDI scheme, a measurement campaign was performed including different road profiles like cobblestone stretch, urban and extra-urban road, and concentrated obstacles with the aim of stimulating different excitation modes in the system. The quantities measured by the sensors were selected on the basis of the Half Car Model [10] which describes the physical behavior of the vertical dynamics of the motorcycle. However, such a model does not allow the steering and the linkage nonlinear effects to be correctly estimated in terms of the corresponding varying wheel base and transfer load, so its equations are valid only in steady-state conditions and cannot be useful for implementing an analytical redundancy-based IFDI scheme which has to operate on-line and in real-time. On the other hand, the behavior of the motorcycle could be modelled by a rigid system, where the rear (S 1 ) and the front (S 2 ) suspension stroke, although dependent from the road profile, are related to the heavy movement of the vehicle frame (S 3 ) and at the longitudinal velocity of the vehicle (S 4 ) [7]. Such considerations highlight a strong correlation between the vertical dynamics quantities for two-wheeled vehicles also in dynamic conditions. Consequently, the signals S 1 -S 4 are directly proportional and indirectly proportional looking to the signals trends, phases and amplitudes, thus allowing to design an IFDI scheme based on simply rules existing among the front and the rear suspension positions (S 2 and S 1 ) as well as the motorcycle pitch and the vehicle speed (S 3 and S 4 ). A qualitative description of the quantities dependency is given by the following functional relationships: 2

4 Figure 2. SRG of the system and VL of the sensors DATA ACQUISITION S M1 S M2 S M3 S M4 S M2 S M3 S M4 S M3 S M1 S M2 S M4 S M1 S M2 (S S1 ) (S S2 ) (S S3 ) (S S4 ) S P1 S P2 S P3 S P4 Res 1 = S M1 S P1 Res 2 = S M2 S P2 Res 3 = S M3 S P3 Res 4 = S M4 S P4 Res 1 Res 2 Res 3 Res 4 DECISION MAKER FAULT/ NO FAULT FAULTY SENSOR Figure 3. Architecture of the proposed IFDI Scheme R 1 : S f S,S,S Eq. (1) R 2 : S f S,S Eq. (2) R 3 : S f S,S Eq. (3) R 4 : S f S,S Eq. (4) As for the identification of f 1, f 2, f 3 and f 4, according to the approach experimented in [7] the equations (1)-(4) could be effectively approximated by NARX networks thanks to their capability of taking into account for the system nonlinearities. 3. Design of the IFDI Scheme As previously said, the analytical redundancy among the quantities measured by the four involved sensors can be exploited for designing a diagnostic scheme able to detect and isolate the faults occurring on such sensors. Then, at first, the nominal diagnostic capabilities of the procedure are evaluated for verifying if the available links (i.e. R 1 -R 4 ) allows to make isolation on all sensors. To these aims the approach suggested in [11]-[12] has been adopted. Figure 2 reports the sensor redundancy graph (SRG) to represent all the knowledge concerning the system and to allow the evaluation of some indexes that measure the IFDI capability of the scheme [11]. In particular, starting from the validity levels of the i-th sensor, VL(S i ), which result all 0, the Percentage of Fault Isolation Degree, PFID %, is given by: PFID % % (5) Such result confirms the capability of making fault isolation for all the considered sensors. 3

5 To this aim, the proposed architecture is sketched in Figure 3. For each measured quantity, S Mi (i=1,..,4), a corresponding predicted value is obtained starting by a combination of the other measured quantities (i.e. a Soft Sensor is setup for each quantity, hereinafter called S Si ). Then, for each quantity, the residual, Res i, is calculated on the basis of the absolute deviation between the measured value, S Mi, and the predicted one by the corresponding Soft Sensor, S Pi. Finally, a suitable decision maker block analyzes the residuals for identifying the faulty sensor when suitable thresholds are overcame (an example is depicted in Figure 4). In the full paper details about Soft Sensors architectures and related optimization as well as the thresholds selection and the diagnostics performance analysis against several kind of faults will be shown. 4. References [1] Betta G and Pietrosanto A 2000 Instrument Fault Detection and Isolation: State of the Art and New Research Trends IEEE Trans. on Instrumentation and Measurement 49 N 1 February 2000 pp [2] Ferreiro García R and Perez Castelo J et alii 2009 A Sensor FDI Strategy for Safety Critical Systems. [3] Catelani M and Ciani L 2014 A fault tolerant architecture to avoid the effects of Single Event Upset (SEU) in avionics applications Measurement Volume 54 pp [4] Capriglione D, Liguori C.and Pietrosanto 2007 A Real-time implementation of IFDIA scheme in automotive system IEEE Trans. on Instrumentation and Measurement Vol 56 N 3 pp [5] Leturiondo U, Salgado O, Ciani L, Galar D and Catelani M 2017 Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data Measurement Volume 108, pp [6] Betta G, Capriglione D, Pietrosanto A and Sommella P 2011 ANN-based sensor fault accommodation techniques IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics & Drives (SDEMPED) 5-8 Sept Bologna pp [7] Capriglione D, Carratù M, Liguori C, Paciello V and Sommella P 2017 A soft stroke sensor for motorcycle rear suspension Measurement Volume 106 pp [8] Capriglione D, Carratù M, Pietrosanto A and Sommella P 2017 ANN-based IFD in Motorcycle Rear Suspension Proc. of 15th IMEKO TC10 Workshop on Technical Diagnostics Budapest Hungary 6-7 June [9] Capriglione D, Carratù M, Pietrosanto A and Sommella P 2018 NARX ANN-Based Instrument Fault Detection in Motorcycle Measurement, Volume 117, pp [10] Spelta C, Delvecchio D and Savaresi M S 2010 A comfort oriented control strategy for semiactive suspensions based on half car model ASME Conference DSCC2010 pp [11] Lee S C 1994 Sensor value validation based on a systematic exploration of the sensors redundancy for fault diagnosis KBS IEEE Trans. Syst., Man, Cybern. pp [12] Betta G, Liguori C and A. Pietrosanto A 2001 Design of automatic measurement stations with fault accommodation capabilities Measurement Vol. 30 no. 2 pp Figure 4. Detection of the miscalibration fault for the rear stroke sensor: normalized output from the hard (blue line) and soft sensor (red line), instantaneous (purple line) and moving average (orange line) value of the residual Res 1 compared against the detection threshold (black dashed line). 4