Test Rig Data Research Articles

Fault detection in rolling element bearings using wavelet-based variance analysis and novelty detection

Fractal signal processing and novelty detection are used for fault detection in rolling element bearings. The former applies the concept of self-similarity based on wavelet variance, and the latter is based on machine learning and utilises artificial neural networks. The method is demonstrated using simulated and experimental vibration data. The work presented involves validation both on laboratory test rig data and industrial wind turbine data. The results show that the method can be used successfully for automated fault detection in ball bearings under real operational conditions.

Experience With Gas Path Analysis for On-Wing Turbofan Condition Monitoring

Gas path analysis (GPA) is an effective method for determination of turbofan component condition from measured performance parameters. GPA is widely applied on engine test rig data to isolate components responsible for performance problems, thereby offering substantial cost saving potential. Additional benefits can be obtained from the application of GPA to on-wing engine data. This paper describes the experience with model-based GPA on large volumes of on-wing measured performance data. Critical is the minimization of the GPA results uncertainty in order to maintain reliable diagnostics and condition monitoring information. This is especially challenging given the variable in-flight operating conditions and limited on-wing sensor accuracy. The uncertainty effects can be mitigated by statistical analysis and filtering and postprocessing of the large datasets. By analyzing correlations between measured performance data trends and estimated component condition trends errors can be isolated from the GPA results. The various methods assessed are described and results are demonstrated in a number of case studies on a large turbofan engine fleet.

Assessment method of sound radiated by cyclically operating Wells turbines

The oscillating water column (OWC) is a system for sea wave energy conversion. This paper deals with the axial Wells turbine. Main objectives of this study are the experimental determination of aerodynamic and aero-acoustic characteristics of a laboratory scale turbine and their subsequent usage for full scale turbine performance prediction.For that a model Wells turbine was built and tested on a laboratory test rig at the University of Siegen. Assuming that the steady-state characteristics hold in unsteady operation, mean quantities such as a mean efficiency and an equivalent sound power level are defined that allow the quantification of the overall performance during cyclic operation. The steady-state quantities are used to assess various control strategies and to predict the aero-acoustic performance of the full scale LIMPET turbine on Islay, Scotland.In general, scaled-up acoustic laboratory test rig data correlates well with measurements from the full scale LIMPET turbine. A theoretical assessment of different control strategies under regular wave conditions demonstrates the potential of a variable speed control for reduction of equivalent sound power. Turbine operation which admits stall yields a typical signature in the equivalent sound power spectrum: a stalled Wells turbine emits low frequency noise at a level far above all levels encountered in the unstalled flow regime and hence significantly contributes to the equivalent sound power.

Calculation of Kinematics and Friction of a Spindle Bearing Using a Local EHL Friction Model

A new calculation method was developed to determine the kinematics and friction in high-speed angular contact ball bearings (spindle bearings) under steady-state conditions. The basic idea of this model is to determine the exact ball kinematics by taking the resulting local friction forces on the ball into account. For this purpose, a simple elastohydrodynamic lubrication (EHL) friction model derived from measurement data of a two-disk test rig considering local relative motions, sum velocities, and contact pressures over the area of the Hertzian contact ellipse is proposed. Depending on the assumed bearing kinematics (pitch and yaw angle and ball and cage speed), the resulting forces and torques on the ball vary. By alteration of the ball kinematics, equilibrium in forces and torques can be achieved in order to predict the actual operation conditions. Experiments indicate that this approach allows for a good approximation of the resulting bearing friction torques and the actual pitch angles. The model is applicable for standard angular contact ball bearings as well as for multipoint contact ball bearings.

Bladed disc crack diagnostics using blade passage signals

One of the major potential faults in a turbo fan engine is the crack initiation and propagation in bladed discs under cyclic loads that could result in the breakdown of the engines if not detected at an early stage. Reliable fault detection techniques are therefore in demand to reduce maintenance cost and prevent catastrophic failures. Although a number of approaches have been reported in the literature, it remains very challenging to develop a reliable technique to accurately estimate the health condition of a rotating bladed disc. Correspondingly, this paper presents a novel technique for bladed disc crack detection through two sequential signal processing stages: (1) signal preprocessing that aims to eliminate the noises in the blade passage signals; (2) signal postprocessing that intends to identify the crack location. In the first stage, physics-based modeling and interpretation are established to help characterize the noises. The crack initiation can be determined based on the calculated health monitoring index derived from the sinusoidal effects. In the second stage, the crack is located through advanced detrended fluctuation analysis of the preprocessed data. The proposed technique is validated using a set of spin rig test data (i.e. tip clearance and time of arrival) that was acquired during a test conducted on a bladed military engine fan disc. The test results have demonstrated that the developed technique is an effective approach for identifying and locating the incipient crack that occurs at the root of a bladed disc.

Optimal sinusoidal modelling of gear mesh vibration signals for gear diagnosis and prognosis

In this paper, the synchronous signal average of gear mesh vibration signals is modelled with the multiple modulated sinusoidal representations. The signal model parameters are optimised against the measured signal averages by using the batch learning of the least squares technique. With the optimal signal model, all components of a gear mesh vibration signal, including the amplitude modulations, the phase modulations and the impulse vibration component induced by gear tooth cracking, are identified and analysed with insight of the gear tooth crack development and propagation. In particular, the energy distribution of the impulse vibration signal, extracted from the optimal signal model, provides sufficient information for monitoring and diagnosing the evolution of the tooth cracking process, leading to the prognosis of gear tooth cracking. The new methodologies for gear mesh signal modelling and the diagnosis of the gear tooth fault development and propagation are validated with a set of rig test data, which has shown excellent performance.

Correlated and Coherent Analysis Based on Vibration Data of Fatigue Life Test Rig for Ball with Three Point Contact

The exciting input signals of the parts in a fatigue life test rig for ball with three point contact and the system’s output signals were analyzed by auto-correlation and cross-correlation with MATLAB software, which eliminated the impact of noise to a certain extent and got the response signals of same frequencies caused by exciting input signals. Finally, the result showed that the text rig was linear by coherent function analysis, which provided the theory basis for modal and dynamic analysis further.

Suspension parameter estimation in the frequency domain using a matrix inversion approach

The dynamic lumped parameter models used to optimise the ride and handling of a vehicle require base values of the suspension parameters. These parameters are generally experimentally identified. The accuracy of identified parameters can depend on the measurement noise and the validity of the model used. The existing publications on suspension parameter identification are generally based on the time domain and use a limited degree of freedom. Further, the data used are either from a simulated ‘experiment’ or from a laboratory test on an idealised quarter or a half-car model. In this paper, a method is developed in the frequency domain which effectively accounts for the measurement noise. Additional dynamic constraining equations are incorporated and the proposed formulation results in a matrix inversion approach. The nonlinearities in damping are estimated, however, using a time-domain approach. Full-scale 4-post rig test data of a vehicle are used. The variations in the results are discussed using the modal resonant behaviour. Further, a method is implemented to show how the results can be improved when the matrix inverted is ill-conditioned. The case study shows a good agreement between the estimates based on the proposed frequency-domain approach and measurable physical parameters.

Numerical Prediction of Two-Phase Reacting Flow Field and Performance in an Aeroengine Combustor

In a three-dimensional arbitrary curvilinear coordinate system, a FORTRAN program was used to research a condition’s influence on an aeroengine’s two-phase reacting flow field and combustion performance. In this program, an algebraic stress model was adopted to simulate turbulence viscosity. A second-order moment eddy–break up turbulence combustion model was used to determine the chemical reaction rate. In a nonstaggered grid system, the governing equations for the gas phase were solved by the PISO algorithm, and simulation of gas-liquid coupling was achieved by using the particle-source-in-cell method. The aeroengine combustor’s performance was estimated by using an empirical-analytical design methodology. It can be observed that performance at maximum conditions is better than that at idling conditions. The obtained agreement between simulation results and rig-test data is reasonable, which indicates the reliability of the numerical program adopted in this paper. This special-purposed program can be used to predict combustor’s flow field and its performance for the optimization design of a modern aeroengine combustor.

Gear Tooth Failure Detection by the Resonance Demodulation Technique and the Instantaneous Power Spectrum Method – A Comparative Study

The role of gears in industry for speed and torque variation purposes is obvious. The gearbox diagnostic methods have been improved quickly in recent years. In this paper, two of the newest methods, the resonance demodulation technique (R.D), and the instantaneous power spectrum technique (IPS) are applied to gearbox vibration signals and their capabilities in fault detection are compared. Yet, the important role of time averaging should not be dispensed with, as it is the primary step for both techniques. In the present study, the mathematical method of these techniques, according to the mathematical vibration model of gears, is introduced, these techniques are applied to the test rig data, and finally the results of both methods are compared. The results indicate that in each method, the location of fault can be estimated and it is located in the same angular position in both methods. The IPS method is applicable to severe faults, whereas the resonance demodulation technique is a simple tool to recognize the fault at each severity and at the early stages of fault generation.

Gear Tooth Failure Detection by the Resonance Demodulation Technique and the Instantaneous Power Spectrum Method – A Comparative Study

The role of gears in industry for speed and torque variation purposes is obvious. The gearbox diagnostic methods have been improved quickly in recent years. In this paper, two of the newest methods, the resonance demodulation technique (R.D), and the instantaneous power spectrum technique (IPS) are applied to gearbox vibration signals and their capabilities in fault detection are compared. Yet, the important role of time averaging should not be dispensed with, as it is the primary step for both techniques. In the present study, the mathematical method of these techniques, according to the mathematical vibration model of gears, is introduced, these techniques are applied to the test rig data, and finally the results of both methods are compared. The results indicate that in each method, the location of fault can be estimated and it is located in the same angular position in both methods. The IPS method is applicable to severe faults, whereas the resonance demodulation technique is a simple tool to recognize the fault at each severity and at the early stages of fault generation.

Efficient Finite Element Analysis/Computational Fluid Dynamics Thermal Coupling for Engineering Applications

An efficient finite element analysis/computational fluid dynamics (FEA/CFD) thermal coupling technique has been developed and demonstrated. The thermal coupling is achieved by an iterative procedure between FEA and CFD calculations. Communication between FEA and CFD calculations ensures continuity of temperature and heat flux. In the procedure, the FEA simulation is treated as unsteady for a given transient cycle. To speed up the thermal coupling, steady CFD calculations are employed, considering that fluid flow time scales are much shorter than those for the solid heat conduction and therefore the influence of unsteadiness in fluid regions is negligible. To facilitate the thermal coupling, the procedure is designed to allow a set of CFD models to be defined at key time points/intervals in the transient cycle and to be invoked during the coupling process at specified time points. To further enhance computational efficiency, a “frozen flow” or “energy equation only” coupling option was also developed, where only the energy equation is solved, while the flow is frozen in CFD simulation during the thermal coupling process for specified time intervals. This option has proven very useful in practice, as the flow is found to be unaffected by the thermal boundary conditions over certain time intervals. The FEA solver employed is an in-house code, and the coupling has been implemented for two different CFD solvers: a commercial code and an in-house code. Test cases include an industrial low pressure (LP) turbine and a high pressure (HP) compressor, with CFD modeling of the LP turbine disk cavity and the HP compressor drive cone cavity flows, respectively. Good agreement of wall temperatures with the industrial rig test data was observed. It is shown that the coupled solutions can be obtained in sufficiently short turn-around times (typically within a week) for use in design.

Hybrid Wireless-Wired Optical Sensor for Extreme Temperature Measurement in Next Generation Energy Efficient Gas Turbines

Accuracy, reliability, and long lifetimes are critical parameters for sensors measuring temperature in gas turbines of clean coal-fired power plants. Greener high efficiency next generation power plants need gas turbines operating at extremely high temperatures of 1500°C, where present thermocouple temperature probe technology fails to operate with reliable and accurate readings over long lifetimes. To solve this pressing problem, we have proposed the concept of a new hybrid class of all-silicon carbide (SiC) optical sensor, where a single crystal SiC optical chip is embedded in a sintered SiC tube assembly, forming a coefficient of thermal expansion (CTE) matched all-SiC front-end probe. Because chip and host material are CTE matched, optimal handling of extreme thermal ramps and temperatures is possible. In this article, we demonstrate the first successful industrial combustor rig test of this hybrid all-SiC temperature sensor front-end probe indicating demonstrated probe structural robustness to 1600°C and rig test data to ∼1200°C. The design of the rig test sensor system is presented and data are analyzed.

Energy saving Actuator Arrangements in an Actively Damped Engine Suspension System

In this work, an active vibration control system is presented that reduces the transmission of engine induced vibrations to the chassis of a vehicle. In order to find system configurations which provide minimum energy consumption, six different configurations of actuator placement with two different actuator types are presented and analyzed regarding their power demands. Models for the test setup are derived and algebraic frequency dependent formulas for the calculation of average mechanical and electrical power consumption are deduced and discussed. The results of the analysis are compared to measurement data of an experimental test rig.

Real Time Estimation of Modal Parameters and Their Quality Assessment

In this paper the recursive method for modal parameters estimation is formulated and verified. Formulated algorithms are implemented in the FPGA electronic chip. As a result, the modal parameters and confidence bounds for the modal parameters are obtained in real time. The algorithms and their implementations are tested on laboratory test rig data and applied to – flight modal analysis of an airframe structure.

Design and performance of thin, circular arc, wind-tunnel turning vanes

Although test rig data exists for ¼ circle turning vanes, the actual performance of these vanes once installed in a wind tunnel, and the extent to which test rig results are replicated, is rarely known. This paper compares pressure loss coefficient and velocity profile data from a vane test module with measurements taken in the low speed wind tunnel described in Ref. 1.

Difference equation approach to two-thermocouple sensor characterization in constant velocity flow environments

Thermocouples are one of the most popular devices for temperature measurement due to their robustness, ease of manufacture and installation, and low cost. However, when used in certain harsh environments, for example, in combustion systems and engine exhausts, large wire diameters are required, and consequently the measurement bandwidth is reduced. This article discusses a software compensation technique to address the loss of high frequency fluctuations based on measurements from two thermocouples. In particular, a difference equation (DE) approach is proposed and compared with existing methods both in simulation and on experimental test rig data with constant flow velocity. It is found that the DE algorithm, combined with the use of generalized total least squares for parameter identification, provides better performance in terms of time constant estimation without any a priori assumption on the time constant ratios of the thermocouples.

Comparison of experiments and reduced-order models for turbomachinery high-incidence flutter

Experimental data and analyses of a reduced-order model are compared. The model is based on simplified physics (actuator disk modelling of bladerows, typical section aeroelastic model) and the measured steady performance characteristic. The model is not intended to be predictive, though it has been shown to be “tunable” to stability measurements of the test rig. The objective of the effort is to estimate the relative sensitivity of flutter damping to system parameters. It is found that flutter damping and stability predictions are in good quantitative agreement with measured test rig data and industry experience. It is shown that the correct trends are predicted for alternate stiffness and stagger mistuning.

Model based Fault Detection for an Active Vehicle Suspension

An automobile fault detection and diagnosis method is described for an active vehicle suspension system. The method is based on mathematical models of the suspension system. It is shown how the unknown parameters of these mathematical models can be obtained experimentally by parameter estimation. Furthermore, the semi-physical approach of the local linear neuronal network LOLIMOT is applied for the generation of parity equations. Both, parameter estimation and parity equations are then used for model based fault detection and identification. Various faults are simulated with test rig data and it is shown, how these faults are detected.

Model based sensor fault detection for an active vehicle suspension

At the beginning, a short introduction into the topic of active vehicle suspensions and the LOLIMOT neuronal network, which is used to identify accurate models of the active suspension system, is presented. Subsequently, physical mathematical models of the considered active vehicle suspension implemented in a test rig are derived. The structure of these nonlinear physical models is then used to define the inputs and outputs of the local linear neuronal network LOLIMOT. Hence, a semi-physical modelling approach results. This network is then trained with test rig data, in order to obtain accurate parity equations for model based fault detection. Various faults are simulated with test rig data and it is shown, how these faults are detected.