Damper Fault Research Articles

Research on fault diagnosis of mechanical component in high-speed train based on one-dimensional convolutional block residual channel attention

The yaw damper and the rolling bearing are key components to ensure the safe and reliable operation of high-speed trains. However, fault identification in these components remains challenging, with traditional feature extraction methods often suffering from low diagnostic accuracy. To address these issues, a one-dimensional convolutional block residual channel attention (1DCBRCA) method for fault diagnosis of high-speed train components is introduced. Firstly, the convolutional layer is constructed for feature extraction, and the convolution block attention module is utilized for adaptive feature optimization in both channel and space dimensions. Subsequently, a residual neural network model is established, with channel attention added after each residual block to focus on critical feature information, which is used for adjusting the weight parameters. Furthermore, the proposed method was validated through damper fault experiments and bearing fault experiments. Comparative analysis with state-of-the-art methods demonstrated that the proposed 1DCBRCA approach achieves higher diagnostic accuracy, confirming its potential to enhance fault diagnosis in high-speed train components.

Fault Estimation Observers for the Vehicle Suspension with a Varying Chassis Mass

This paper proposes a descriptor Nonlinear Parameter Varying (NLPV) observer for damper fault estimation in vehicle semi-active suspension systems, here considering the highly practical case of a varying sprung mass, which is an open problem. This mass and the control input are treated as known scheduling parameters for the design and implementation of the observers, thus making the structure adaptive. This novel modeling leads to parameter-dependent dynamics and output matrices in the nonlinear parameter-varying formulation. To solve this problem, we extend an existing reduced-order observer design method (following descriptor system modeling) to this new case. Both polytopic and grid-based methods are then considered to compute and implement the problem solutions. The designs are then assessed using frequency-domain analysis and realistic time-domain simulations.

Bilinear observer-based robust adaptive fault estimation for multizone building VAV terminal units

In this paper, we propose a novel bilinear observer-based robust fault detection, isolation and adaptive fault estimation methodology to precisely estimate a class of actuator faults, namely bias in damper position and lock-in-place faults, in Variable-Air-Volume (VAV) terminal units of Heating Ventilation and Air-Conditioning (HVAC) systems. The proposed adaptive fault estimator is robust in the sense that the fault estimates are not affected by the unmeasured disturbance variable and that the effects of measurement noises on fault estimates are attenuated. The fault estimation algorithm with the integrated building control system improves occupants comfort and reduces the operation, maintenance, and utility cost, thereby reducing the impact on the environment. The effectiveness of the methodology for adaptive estimation of multiple or single VAV damper faults is successfully demonstrated through different simulation scenarios with SIMBAD (SIMulator of Building And Devices), which is being used in industries for testing and validation of building energy management systems.

Feasibility study of a rail vehicle damper fault detection by artificial neural Indexed by: networks

The aim of the study was to investigate rail vehicle dynamics under primary suspension dampers faults and explore possibility of its detection by means of artificial neural networks. For these purposes two types of analysis were carried out: preliminary analysis of 1 DOF rail vehicle model and a second one - a passenger coach benchmark model was tested in multibody simulation software - MSC.Adams with use of VI-Rail package. Acceleration signals obtained from the latter analysis served as an input data into the artificial neural network (ANN). ANNs of different number of hidden layers were capable of detecting faults for the trained suspension fault cases, however, achieved accuracy was below 63% at the best. These results can be considered satisfactory considering the complexity of dynamic phenomena occurring in the vibration system of a rail vehicle.

Takagi–Sugeno Fuzzy Observer-Based Magnetorheological Damper Fault Diagnosis Using a Support Vector Machine

In this article, a magnetorheological (MR) damper fault diagnosis algorithm is proposed. Recently, various studies have been proposed fault diagnosis methods for this damper system, however, these methods use a displacement sensor in addition to the two accelerometers mounted on the commercial vehicle. The use of this sensor configuration limits the application of the algorithm proposed so far to commercial vehicles. In order to overcome this limitation, this article proposes an MR damper fault diagnosis algorithm that uses only two accelerometers. Based on these sensors, the states of the vehicle suspension system are estimated by a Takagi-Sugeno (T-S) fuzzy unknown input observer. This observer scheme can estimate the states of the damper even under conditions affected by damper hysteresis and unknown road elevation. Using the Lyapunov stability theorem, the stability of the proposed observer with an unmeasured premise variable is verified. In addition, a support vector machine (SVM) classifier is used to determine damper condition without empirically set thresholds. In this article, a fault flag is generated by data-driven machine learning algorithms, reducing the design effort while at the same time achieving optimal performance. The proposed algorithm is verified using a quarter-car test rig and test results confirm that the proposed algorithm exhibits robust performance in various road conditions. Consequently, the MR damper fault diagnosis algorithm proposed in this article can reduce the effort in designing a diagnosis algorithm while using inexpensive production sensors applied to a vehicle.

Non Linear Parameter Varying Observer based on Descriptor Modeling for Damper Fault Estimation

This paper proposes an H∞ Non Linear Parameter Varying (NLPV) observer for fault estimation in semi-active Electro-Rheological (ER) suspensions. The damper fault (a loss-of-efficiency factor) is modeled as a lost force of unknown/free dynamics to be estimated. Thanks to the parameter-dependent descriptor-form system modeling, there is no assumption made on the fault dynamics, thus making this method applicable to all considered types of damper faults. The nonlinearity in the damper model is bounded by its Lipschitz property, while the road disturbance and the measurement noise are handled using the H∞ condition. The observer is parameterized and then designed by solving Linear Matrix Inequalities (LMIs) and is implemented in a polytopic gain scheduling approach. Synthesis results including Bode plots and simulations illustrate the method in both the frequency and the time domains.

Multi-objective Grid-based Lipschitz NLPV PI Observer for Damper Fault Estimation

Dampers in semi-active suspension systems may be subject to various types of damper faults, e.g., oil leakage and electrical issues, that need to be estimated for diagnosis and isolation purposes. A new method to design the so-called multi-objective grid-based Lipschitz Nonlinear Parameter Varying (NLPV) Proportional Integral (PI) observer is here developed for damper fault estimation, where the fault is the loss of efficiency of the damper modeled as a slow-varying input. While the damper nonlinearity is bounded by the Lipschitz condition, the H∞ and generalized H2 conditions are used to minimize the effects of the input disturbance and the measurement noise, respectively, on the estimation error. Moreover, the observer is designed with Linear Matrix Inequalities (LMIs) formed and solved in a grid-based manner (considering a parameter-dependent Lyapunov function) to reduce the level of conservatism. Analyses in the frequency domain using Bode plots as well as in the time domain using realistic simulations illustrate the effectiveness of the proposed observer.

An adaptive sliding mode fault-tolerant control for semi-active suspensions with magnetorheological dampers based on T-S fuzzy vehicle models

This paper investigates an improved adaptive sliding mode fault-tolerant control strategy for a magnetorheological semi-active suspension system with parametric uncertainties and actuator faults. Using the experimental data collected by a quarter-vehicle test rig, an adaptive-network-based fuzzy inference system is employed to establish a learning-based magnetorheological damper model firstly. The Takagi-Sugeno fuzzy approach is introduced to deal with the uncertainties of sprung mass and pitch rotary inertia and then the corresponding Takagi-Sugeno faulty semi-active suspension system is constructed. An adaptive sliding mode fault-tolerant controller is proposed, in which the magnetorheological damper fault gain is observed by the designed estimation law, and the asymptotical stability of the system is further analyzed. Finally, numerical simulation tests are conducted to demonstrate the effectiveness of the designed control scheme.

Seismic adaptive control of building structures with simultaneous sensor and damper faults based on dynamic neural network

AbstractThe use of seismic control systems in building structures requires simultaneous attention to uncertainties in model parameters, measurement noises, faults on sensors and dampers, and earthquake input. The present study suggests a procedure to resolve the complications of these issues in semi‐active control of building structures. To this end, the effects of faults on sensors and dampers are decoupled by using transfer matrices. The sensor fault and the structure's state are estimated by a dynamic neural network (DNN)‐based observer. Later, parametric uncertainty in the structure's model and fault on dampers are atoned by an adaptive controller that guarantees the stability of the system during an earthquake. In this controller, DNN is used to concurrently identify and compensate dampers' faults. The effectiveness of the proposed method was confirmed by numerical simulation of a three‐story structure with magneto‐rheological dampers.

Evaluation of the Damper Condition in Synchronous Motors Through the Analysis of the Transient Stray Fluxes and Currents Considering the Effect of the Remanent Magnetism

This article proposes the qualitative and quantitative analysis of stray-flux and current data under starting to detect damper faults in cylindrical rotor synchronous machines. These machines are typically employed in high power applications and their possible outages may imply huge costs for the industries or plants where they operate. The damper cage is a critical part of these machines and a potential source of catastrophic failures. However, few research works have provided feasible alternatives to monitor the condition of such element. This article analyses the viability of analyzing the electromotive force signals induced by the stray-flux in external coil sensors as well as current signals under starting to diagnose damper faults. The results obtained with laboratory machines with different levels of damper damage show that the analyses of those signals can provide very useful information for determining how the damper degrades over time. Moreover, the article studies the effect of the remanent magnetism over the viability of the approaches and provides solutions to overcome this problem. The conclusions are valuable for field engineers since, nowadays, there are few available solutions that allow monitoring the condition of such element without motor disassembly.

Bilinear model-based diagnosis of lock-in-place failures of variable-air-volume HVAC systems of multizone buildings

Centralized heating, ventilation, and air-conditioning (HVAC) systems equipped with variable-air-volume (VAV) terminal boxes are widely used for energy-efficient control in multizone buildings. Any occurrence of faults in VAV boxes drastically hampers the control performance. In this paper, we present a novel bilinear observer-based fault diagnosis (FD) algorithm for detection and accurate estimation of the magnitude of the lock-in-place failure of VAV dampers, which plays a significant role in the supervisory health-aware control strategy. The key contribution in the proposed FD algorithm is that instead of using specific linearized models, the algorithm is inherited from the true bilinear model structure of the building thermal dynamics. As an immediate consequence, the algorithm is applicable over a wide range of unpredictable operating conditions, namely, weather dynamics, outside air temperature, zone occupancy profile. Moreover, a practical cost-effective feature of the derived underlying bilinear observer is the estimation of wall temperatures, which are generally essential but unmeasured state variables with regards to climate control in buildings. The effectiveness of the proposed algorithm demonstrating the successful detection and estimation of VAV multiple damper faults are validated through exhaustive simulations with SIMBAD (SIMulator of Building And Devices).

Fault detection in commercial building VAV AHU: A case study of an academic building

The building sector of the U.S. currently consumes over 40% of the U.S. primary energy supply. Estimates suggest that between 5% and 30% of any building's annual energy consumption is unknowingly wasted due to pathologically malfunctioning lighting and comfort conditioning systems. This paper presents analytical methods embodied within useful software tools to quickly identify and evaluate selected faults in air handling units (AHU) that cause large building energy inefficiencies. Algorithm for faults like stuck dampers and leaking dampers have been developed and tested in this particular case study. These damper fault detection algorithms can be applied to outdoor air and return air dampers both. The technical contributions of this work include expert rules that adapt to the Heating, Ventilation and Air-Conditioning (HVAC) equipment scale and operation and methods for sorting fault signals according to user-defined interests such as annual cost of energy inefficiencies. The combination of expert-rule based fault detection combined with first principles thermodynamic modeling is a unique contribution of this work that leads to quicker fault detection with minimal non-intrusive measurements. These contributions are particularly unique in their treatment of models and the careful consideration of user-interests in fault evaluation. As a first step to developing this general framework for fault detection, first-order faults such as stuck dampers and imbalanced airflows within several large air-handling units were targeted. The algorithms focused on detecting faults with minimal data and non-intrusive measurements. An example is presented of the potential energy savings in a large academic building that has been monitored. Real data from an academic building in Boston was collected from the building energy management system (BEMS) for the purpose of this study. Savings of around $3400/month or 18% of the monthly cost when a stuck damper fault occurred over an entire month in an air handler. Another fault that was investigated was that of leaking dampers which lead to savings of approximately $500/month or around 2.5% of the monthly energy expense. The savings would accrue if the fault were corrected; otherwise the occurrence of the fault causes a waste of money and energy predicted. The algorithms developed can be applied to large commercial office buildings, academic buildings, hospitals and other modern buildings in various climates as long as they have data collecting capabilities.

A novel semi-active control strategy based on the quantitative feedback theory for a vehicle suspension system with magneto-rheological damper saturation

This paper presents a robust controller for a semi-active suspension system with actuator saturation. It addresses the vehicle vibration attenuation problem under two cases: (i) without actuator fault (magneto-rheological (MR) damper) and (ii) with base oil leakage in MR damper. The solution is obtained using the quantitative feedback theory (QFT) design. The actuator (MR damper) dynamics is well approximated by a first order model with an uncertain time constant. The superior performance of the proposed QFT controller, for fault free case, is compared with the sky-hook, H∞ control and passive suspension in time as well as in frequency domains. To address the system with faulty MR damper, a multiple model approach based QFT design is then proposed. The multiplicative type fault is considered as an MR damper fault and the faulty damper output delivering capability is then reduced by 80% to 75% due to the oil leakage in the damper. The proposed idea is centered on dividing the large uncertain system into a set of small uncertain linear system and then design the QFT controller corresponding to each of the generated linear models. The global controller is formulated by aggregating the local controller with the gap metric based weights. This new approach produces the similar performance as that of the passive fault tolerant control (FTC)-QFT design with less control effort. Extensive comparative simulations are provided to show the efficacy of the proposed method over passive FTC-QFT, passive FTC-H∞ design and sky-hook control.

Modeling, diagnosis and estimation of actuator faults in vehicle suspensions

This paper deals with the modeling, diagnosis and estimation of faults in automotive Semi-Active (SA) dampers, particularly oil leakages in the actuator. An experimental multiplicative fault model is proposed and statistically validated with an index error of 15% for damper leakage. The fault model is used as design basis for two Fault Detection and Isolation (FDI) frameworks. The Frequency-based Fault Estimator (FFE) is based on the effect of the damper fault in the frequency domain and the Robust Parity Space (RPS) consists in a residual generator sensitive to the fault in the time domain. The model-based FDI systems were experimentally validated in a 1:5 scaled vehicle, fully instrumented and equipped with SA dampers. The experimental results show that, while both approaches represent suitable options for commercial applications, the RPS estimator has the fastest detection time and proportionality to the fault level. In addition, the RPS approach has better robustness to vehicle mass uncertainties. On the other hand, the FFE presents lower sensitivity to road profile and semi-active damper input variations. Additionally, this estimator requires a lower number of sensors and has a lower computational overhead.

Using a hybrid method to construct a computational efficient cooling coil model for an automated single-duct variable air volume system fault detection and diagnosis

This paper introduces a hybrid method that integrates physical laws, system knowledge and real-time measured data in order to develop a computational efficient cooling coil model that can detect air-handling unit (AHU) system operation faults and inefficiencies. A thermal balance model to describe the cooling energy use of an AHU is built based on the first law in order to understand equipment mechanisms and to determine the variables that impact cooling coil energy performance. In order to avoid humidity measurements of supply air and return air that are not readily available in AHU operations, the energy balance model is then simplified into a lumped temperature-based model for dry coil operations and a lumped enthalpy-based model for wet coil operations. Parameters in the lumped models are determined using the least squares method along with short periods of measured data. Through experiments, the proposed cooling load baseline produced ±5.5% errors at 95% confidence on an AHU with a cooling capacity of 28kW (8t). The effectiveness of fault detection using the studied cooling model is also tested by introducing a stuck outdoor air damper fault on the test AHU.

An LPV Fault Tolerant control for semi-active suspension - scheduled by fault estimation

In this paper, a novel fault tolerant control is proposed to accommodate damper faults (oil leakages) in a semi-active suspension system based on a quarter-car vehicle model. The fault accommodation is based on the Linear Parameter Varying (LPV) control strategy and involves two steps. At first, a fast time-varying fault is estimated by using the fast adaptive fault estimation (FAFE) algorithm, based on an unknown input adaptive observer. Thanks to information about the estimated fault, the dissipativity domain of the semi-active suspension is adapted according to the fault. Then a single LPV fault tolerant controller is developed to manage the system performances. The controller solution, derived in the LPV/H∞ framework, is based on the LMI solution for polytopic systems. Some simulation results are presented that show the effectiveness of this approach.

Fault Detection of Vertical Dampers of Railway Vehicle Based on Phase Difference of Vibrations

This paper presents a technique for damper fault detection based on the phase difference between the bounce and pitch motions of bogie frames or a car body obtained with inertial sensors. This method avoids the need for mounting sensors in oil dampers. The result of vibration excitation tests with one vehicle on a testing plant demonstrated that the faults in a primary vertical damper were detectable by evaluating the phase difference between the bounce and pitch motions of the bogie frames. The results of running tests on a meter-gauge line demonstrated that secondary vertical damper faults were also detectable based on the phase difference between the bounce and pitch motions of the car body.

Fault Detection of Dampers of Railway Vehicles Focused on Phase Difference between Translational and Rotational Motion

The authors have developed a vibration suppression system with variable vertical oil dampers in primary or secondary suspensions of railway vehicles for improvement of riding comfort due to vertical vibration. In order to apply this system into practical use, the function to detect the failure and stop the control in case some damper fails is required. This paper presents a technique for damper fault detection based on the phase difference between the bounce and the pitch vibration of bogie frames or car bodies obtained with inertial sensors, as a method without mounting sensors in oil dampers. The result of vibration excitation test with one vehicle on a testing plant demonstrates the fault of a primary vertical damper was detectable by evaluating the phase difference between the bounce and the pitch vibration of the bogie frames. The result of running test in a meter-gauge line demonstrated the fault of a secondary vertical damper was detectable based on the phase difference of that of car bodies. These results have suggested the proposed method is effective for the fault detection of both primary and secondary vertical damper.

Integrated Control and Fault Detection of Air-Handling Units

A new method for integrated control and fault detection of air-handling units (AHUs) is described in this paper. The method uses sensors commonly installed in AHUs and collects much of the key diagnostic information at times when steady-state conditions are imposed on the AHU by the sequencing logic, thereby eliminating the need for a steady-state detector. A model-based fault detection method processes these data and generates residual values that can be further processed to detect faults. For faulty operation, one or more of the residuals is expected to have a value that is significantly different from 0, the expected value for normal operation. In parallel to model-based residual generation, an algorithm calculates state-based performance indices of control loops for the processes used for sequential control of the AHU. The method was assessed through simulations of 16 faults, which consisted of temperature sensor offset faults, stuck and leaking damper faults, and stuck and leaking valve faults. With the exception of the return air temperature sensor offset faults, each fault impacted numerous residuals. In addition, two faults (stuck-closed recirculation air damper and heating coil valve stuck 10% open) caused control performance indices to saturate, which should not happen for a properly designed and operating system. The complementary aspect of the model-based residuals and state-based performance indices is described and illustrated with these faults.

A model-less technique for the fault detection of rail vehicle suspensions

This paper presents a novel method for the fault detection and condition monitoring of rail vehicle suspensions. The proposed technique takes advantage of the vehicle (suspension) configurations that are often symmetrical, and explores the additional dynamic interactions between different motions of a bogie or body caused by the failure of suspension components. The basic principle of the proposed detection method is presented and the interactions due to suspension fault conditions are analysed using a conventional two-axle bogie. Side-view models of a bogie vehicle are used in the study to demonstrate the effectiveness of the novel method in detecting damper faults in the suspensions. Both linear and bi-linear dampers are studied.