An integrated prognostic and condition monitoring strategy for primary flight control electro-mechanical actuators

03/02/2015
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An integrated prognostic and condition monitoring strategy for primary flight control electro-mechanical actuators

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application/pdf An integrated prognostic and condition monitoring strategy for primary flight control electro-mechanical actuators Thu-Hien Pham, Jens Windelberg, Andreas Bierig
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An integrated prognostic and condition monitoring strategy for primary flight control electro-mechanical actuators

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An integrated prognostic and condition monitoring strategy for primary flight control electro-mechanical actuators Thu-Hien Pham (1), Jens Windelberg (2), Andreas Bierig (3) 1 : DLR, Lilienthalplatz 7, 38108 Braunschweig, thu-hien.pham@dlr.de 2 : DLR, jens.windelberg@dlr.de, 3 : DLR, andreas.bierig@dlr.de Abstract The use of electro-mechanical actuators (EMAs) in aircraft is considered for many decades [1] [11] [13] [14]. Despite their advantages over traditional hydraulic actuators, there is no well-elaborated concept to provide an equivalent level of safety, eliminating the risk of EMA to jam. Thus, EMAs are still not used in primary flight control systems. Failures mostly develop over a long time and early fault detection usually addresses the safety issue. Condition-based maintenance is a concept that has been applied successfully in other branches to prevent failures, e.g. wind energy. This paper presents a strategy of an integrated prognostic and condition monitoring concept to provide efficient maintenance for aircraft EMAs, covering the most safety critical failure cases of that application. The strategy structure is given, showing the reasonable connection between components and stating the principle idea. Besides, a brief review and analysis of recommended condition monitoring methods and techniques is provided. Introduction The availability of high performance power electronics as well as of rare earth magnet materials made electrical drives attractive for many applications where high power density is required. Recent aircraft programs, such as Boeings 787 Dreamliner already utilize electromechanical actuators due to their high potential of weight saving and efficiency enhancement. Besides the mentioned advantages, the main preventers of utilizing electromechanical actuators in primary flight control systems are their safety and reliability issues. Due to a high number of mechanical contacts necessary to transmit forces and moments from the electrical drive to the control surface, a distinct risk of jamming as well as of disassembly in case of any mechanical failure is present. Both faults have a significant potential to cause a catastrophic, but a least a major incident. Compared to conventional hydraulic flight control actuators the reliability of EMAs is still not proven to be at the same level [1]. The general idea is based on the assumption that most of the mechanical failure processes, which may lead to a mechanical fault, do develop over a long time period. In this case the probability of the occurrence of faults leading to flight critical situations can be reduced significantly, if the failure process is detected early enough. To detect failure processes, condition monitoring systems (CMS) are widely utilized in other industry, such as wind power generation. Thereby the wind power plant is continuously observed by the CMS and alarms are triggered, when a critical level of a condition indicator (CI) is reached [2]. An example from the aerospace sector Health and Usage Monitoring Systems (HUMS) may be mentioned, which monitor flight critical components of helicopters [3]. Condition-based maintenance can help repairing and replacing defected component before failure and preventing catastrophic events. For safety critical flight control EMAs, the risk of jam is reduced, if replacement is induced based on the diagnosis of a detected fault. Due to the fact that diagnostics output the instantaneous state of the EMA, but do not provide any time information towards the future failure event, an early replacement can be inefficient in operation cost. Prognostic-condition-based maintenance moreover can save overall operational cost, taking in to account that mechanical faults approximately need the predicted Remaining Useful Life (RUL) time to run to failure. However, the maintenance must be planed as a trade-off between safety and cost. The design of CM and prognostic systems is a challenging task and still subject of intensive research. The approaches presented in this paper will help to design such systems efficiently, by linking simulation models and test bed evaluations of real EMA components together. Prognostic and condition monitoring strategy The prediction of the health state over the time is the main issue of prognostics, including the calculation of RUL. As far as mechanical systems are concerned, prediction is still challenging, because of many influencing factors (stress, fault appearance, environment, etc.). Under real operation mode, a mechanical system like an EMA underlies an individual stress profile, which is mainly responsible for its instantaneous state, and cannot be accurately determined from the beginning of use. Besides, faults of root causes like overloading, vibration, foreign particles, inadequate lubrication, and passage of electric current can appear and accelerate the life time of an EMA [4]. The effect of all influencing factors shows in the degree of fatigue as visible anomalies [3]. CIs indicate this development, since machines in operation are not supposed to be deconstructed and remote monitoring is required. The main concern dealing with prognostic is how to form the so called health-parameter (HP) using CIs as a function of time. The HP function determines the condition state, when the time is given. From the perspective of time, the accuracy of HP-functions depends on the a posteriori stress profile and the future stress profile. The accuracy of the CIs however depends on how condition monitoring (CM) is configured for a certain system. CM systems utilize several sensors to generate raw data and numerous mathematical methods to process them. By means of these processing methods, subsequently features can be extracted and CIs can be computed, subsequently. This paper presents an integrated prognostic and condition monitoring strategy, its principle is shown in Fig. 1. Fig. 1: Integrated prognostic and condition monitoring strategy for primary flight control EMA A complete chain from data generation to RUL calculation is illustrated in Fig. 1, except the fact that Generic EMA Modell and Fault Progression Modell form an iterative process. In this, the conventional data generation through experimental tests represents one input of the chain. For this purpose, a CM test bed for flight control EMA is constructed to reproduce data of different mechanical fault constellations. The second input of this chain is provided by a flexible fault model of an EMA, capable to deal with different EMA structures. The Condition Monitoring component of this strategy represents the center between input data and the subsequent component Prognostics. Each component of the strategy has its own logic and can be independently utilized. Thus, the meaning of these components are successively explained successively in following sections, whereat prognostic is not one of the issues of this paper, but the ideas leading towards this topic. Experimental test To generate EMA measurement data at different level, a component assembly test rig of direct drive principle and a test rig, capable to deal with different EMA structures, were constructed. The idea behind the first test rig is to flexibly test mechanical components of a reference EMA. This flexibility is required to test a number of nominal and faulty components by inserting typical faults at single sample. The second test rig is needed to test real EMAs. Both test rigs have the possibility to apply continuously time transient load and motor speed as well as linear position of the EMA end rod. This configuration of a test rig allows test being driven by a load and a position or motor speed profile. These test profiles can be simulated or obtained from a real flight. This section shows how extensive tests can be, to create a representative data base. In Addition, the advantages and disadvantages of experimental tests are stated. Generic EMA Model & Fault Progression Model To be able to validate Condition Monitoring and Prognosis techniques representative data is needed. Problems encountered for generating these data from test rig operations within a reasonable amount of time and budget are explained. To overcome this, a modelling approach is pursued for the integrated strategy. To derive the requirements for such a model, a closer look at EMA- components for primary flight controls and its working conditions is taken. Based on these requirements and based on the experience that already has been gathered in industrial environment under similar conditions (available for most of the components in literature, e.g. for bearings with failure modes [5]), a model is derived. Subsequently, features for a physical fault model are extracted. The effects and properties that are responsible for or have a relevant influence on the conduction of fault features within the actuator and as a consequence should be accounted for in the model are stated. Finally modelling methods for the different components, concerning for example contact theory, structural dynamics or electromagnetic coupling known from literature are presented and compared concerning their usefulness for the EMA-model. The component models are then joined together in a dynamic multi body system. To achieve interchangeability for different parts and by that to gain a generic model a possibility to define interfaces in a Simulink environment is proposed. The resulting detailed model can then be used to generate virtual measurement data for a precise fault on the one hand, and on the other hand also to compute localized stresses, for example in contacting areas. To be able to estimate the fault size growth based on physical motivated models, literature of this domain is analysed. EMA Condition Monitoring From a CM point of view, there are aspects to be considered comprising the structure of the target machinery, the methods and techniques as well as and the operation in the application environment. nr component Literature source Methods DIs / PP 1 bearing SD [6] statistical methods RMS 2 Variance 3 Kurtosis 4 Crest factor 5 PSA 6 M6 screws SD - - 7 PMSM SD [7] MCSA spectral data 8 bearing [8] EA energy ratio 9 PMSM [9] Stator current DWT energy of DWT details screws - - - SD Standard Diagnostic DI Diagnostic Indicator PP Prognostic Parameter RMS Root Mean Square M6 sixth statistical Moment PSA Peak Signal Amplitude MCSA Motor Current Signature Analysis EA Envelope Analysis DWT Discrete Wavelet Transform Table 1: feature extraction techniques for prognostics EMAs for flight control systems can have different structures to carry the functionality as rotary or linear drive machine. However, the components are in general an electric motor, gearbox, bearings and rotor shaft [4]. Whereat, the gearbox is not included for every EMA structure. The most popular EMA structure is the direct-drive structure including the permanent magnet synchronous motor (PMSM) [10] [11]. Electrical faults as well as mechanical faults can lead to failure, but one important difference between them is their probability of appearance. As presented in [1], mechanical faults have a higher probability to fail then electrical faults, for actuation systems in commercial aircraft. For EMAs and electric machines in general, the percentage of mechanical faults is higher than 50 % and almost 40% are bearing-related [11] [12]. The strategy presented in this paper is devoted to mechanical faults and explain the effect of faults to each component in an example of direct- drive EMAs. The effect of faults can be captured as an observable quantity, such as motor current, vibration or acoustic emission, by means of applicable transducers. The reliable combination of quantity and transducer is called CM method. A number of methods have been used to monitor machinery in general as well as EMAs is special case, and thus, this paper presents a collection of those having high relevance to monitor primary flight control EMAs. The condition of a machine is describable using CI. To compute CIs, features of measurement or simulated data are used. A process, which receives data as input and outputs the features, is called feature extraction, comprising the necessary feature extraction techniques. An overview of these techniques is given in this section (Table 1). The operation conditions and the environmental conditions are the most important aspects of EMAs to be investigated, when the proof of their reliability is intended. This section explains how these aspects influence the possible feature extraction techniques. Discussion As presented in previous sections, there are a number of challenges according following aspects:  general CM,  EMA structure,  Data generation  CM methods,  signal processing techniques,  operation on an aircraft  environment Restrictions due to the EMA structure, the operation mode and the environment lead to limitation for single CM methods, such as those dealing vibration and using acoustic emission. This section also discusses the potentials of methods that do not get affected by application’s typical influencing factors. An overview of all findings can be shown in a diagram of a frequency dependent fault evaluation. This diagram can clearly show the limitation of each recommended CM methods as well as the limitation of the data generation. Conclusions Due to the limitations, recognizable through discussion, an overall statement about the suitability of the strategy for primary flight control EMA, introduced in this paper, can be made. In this, the use of the stated strategy, using standard feature extraction techniques, is limited to pre-flight check. To make this strategy reliable for the complete flight mission, the capability of sensor concepts need to be proven and future feature extraction techniques need to be developed further. In Addition, the flexibility of the Generic EMA Model and the availability of the Test Rigs provide a dual platform for different EMA structures to be investigated on one hand and novel CM methods to be developed on the other hand. References 1 Bennett et al, Safety-critical design of electromechanical actuation systems in commercial aircraft, IET Electr. Power Appl., 2011, Vol. 5, Iss. 1, pp. 37–47 2 Germanischer Lloyd, Guideline for the Certification of Condition Monitoring Systems for Winturbines, Edition 2013 3 He and Bechhoefer, Development and Validation of Bearing Diagnostic and Prognostic Tools using HUMS Condition Indicators, IEEE. 2008 4 Wagner et al, Challenges for Health Monitoring of Electromechanical Flight Control Actuation Systems, SAE Int. J. Aerosp. 4(2):1315-1323, 2011 5 Schaeffler Technologies AG & Co.KG, Wälzlagerschäden Schadenserkennung und Begutachtung gelaufener Wälzlager, 2013 6 Lybeck et al, Validating Prognostic Algorithms: A Case Study Using Comprehensive Bearing Fault Data. IEEEAC paper #1052, Version 2, 2006 7 Benbouzid. A Review of Induction Motors Signature Analysis as a Medium for Faults Detection, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 47, NO. 5, 2000 8 Li et al, Fault features extraction for bearing prognostics, Springer Science+Business Media, LLC. 2009 9 Romeral et al, Electrical monitoring for fault detection in an EMA. IEEE A&E SYSTEMSMAGAZINE, 2010 10 Garcia et al, Reliable Electro-Mechanical Actuators in Aircraft, IEEE A&E SYSTEMS MAGAZINE, 2008 11 Jänker et al, New Actuators for Aircraft and Space Applications. ACTUATOR 2008, 11th International Conference on New Actuators, Bremen, Germany, 2008 12 Tavner, Review of condition monitoring of rotating electrical machines, IET Electr. Power Appl., 2008 13 Lear, Remote and automatic electric controls for aircraft, Joint Meeting of The Franklin Institute and The American Institute of Electrical Engineers, Philadelphia Section, I944. 14 Rosero et al, Moving Towards a More Electric Aircraft. IEEE A&E SYSTEMS MAGAZINE, MARCH 2007