Suspension Faults Research Articles

A Deep-LSTM-Based Fault Detection Method for Railway Vehicle Suspensions

The timely detection of faults that occur in industrial machines and components can avoid possible catastrophic machine failure, prevent large financial losses, and ensure the safety of machine operators. A solution to tackle the fault detection problem is to start with modeling the condition monitoring signals and then examine any deviation of real-time monitored data from the baseline model. The newly developed deep long short-term memory (LSTM) neural network has a high nonlinear flexibility and can simultaneously store long- and short-term memories. Thus, deep LSTM is a good option for representing underlying data-generating processes. This paper presents a deep-LSTM-based fault detection method. A goodness-of-fit criterion is innovatively used to quantify the deviation between the baseline model and the newly monitored vibrations as opposed to the mean squared value of the LSTM residual used in many reported works. A railway suspension fault detection case is studied. Benchmark studies have shown that the deep-LSTM-based fault detection method performs better than the vanilla-LSTM-based and linear-autoregression-model-based methods. Using the goodness-of-fit criterion, railway suspension faults can be better detected than when using the mean squared value of the LSTM residual.

Dijagnostika kvarova zasnovana na bestežinskoj neuronskoj mreži u sistemu vešanja

Vehicle suspension systems play a critical role in ensuring passenger comfort and safety. Detecting faults in these systems is vital for maintaining safety, performance, and cost-effectiveness. Traditional inspection methods have limitations, such as visual checks, bounce tests, and alignment assessments. This study explores Wilkie, Stonham, and Aleksander Recognition Device (WiSARD), a weightless neural network (WNN), for suspension fault diagnosis. A WNN model is employed to classify suspension system faults using sensor data. The dataset includes both normal and faulty conditions to train the model. The study assesses WiSARD under various fault conditions, including strut damage, mount failure, worn-out components, and low wheel pressure. Comparative evaluations demonstrate that the approach outperforms other classification techniques, achieving an impressive 95.63% accuracy with a rapid 0.05-second computation time for test data. This WNN-based method proves superior in detecting suspension faults and holds potential as a candidate for real-time vehicle fault diagnosis systems.

Transfer Learning Based Fault Detection for Suspension System Using Vibrational Analysis and Radar Plots

The suspension system is of paramount importance in any automobile. Thanks to the suspension system, every journey benefits from pleasant rides, stable driving and precise handling. However, the suspension system is prone to faults that can significantly impact the driving quality of the vehicle. This makes it essential to find and diagnose any faults in the suspension system and rectify them immediately. Numerous techniques have been used to identify and diagnose suspension faults, each with drawbacks. This paper’s proposed suspension fault detection system aims to detect these faults using deep transfer learning techniques instead of the time-consuming and expensive conventional methods. This paper used pre-trained networks such as Alex Net, ResNet-50, Google Net and VGG16 to identify the faults using radar plots of the vibration signals generated by the suspension system in eight cases. The vibration data were acquired using an accelerometer and data acquisition system placed on a test rig for eight different test conditions (seven faulty, one good). The deep learning model with the highest accuracy in identifying and detecting faults among the four models was chosen and adopted to find defects. The results state that VGG16 produced the highest classification accuracy of 96.70%.

Robust early detection of hunting on railway vehicles: single-sensor based methods and critical comparisons

ABSTRACT The problem of robust and early detection of railway hunting is considered and two single-sensor ‘dynamics-based’ methods employing adaptive Recursive AutoRegressive (RAR) modelling of the car-body lateral random vibration signal are postulated. The first is based on a Degree-of-Stochasticity (DS) measure of the vibration signal and the second on the minimum underlying Damping Ratio (DR). Fully automated tuning for both methods is achieved via Bayesian optimization. The performance of the methods is systematically assessed via high numbers of Monte Carlo simulations under various Scenarios employing a high-fidelity SIMPACK-based vehicle model and three performance criteria: the True Positive Rate (TPR), the False Positive Rate (FPR), and the Detection Delay Time (DDT) with respect to conventional hunting initiation. The results reveal very good performance and robustness to suspension faults and worn track conditions for both methods, with the DR-based exhibiting an edge. Its performance and achievable robustness are also shown to be clearly superior to those of three alternative methods.

METHODS FOR MONITORING THE TECHNICAL CONDITION OF THE SUSPENSION OF VEHICLE UNDER OPERATING CONDITIONS

Suspension faults can reduce wheel traction and therefore reduce vehicle stability. Existing diagnostic methods for the suspension do not take into account the stability of the vehicle. To control stability, it is proposed to use road and bench methods, characterized by suspension vibrations and constant lateral force. Theoretical studies were carried out using mathematical models based on the description of the functioning of sprung and unsprung vehicle masses, as well as elastic tires. Experimental studies include bench tests of tires, shock absorbers and silent blocks of various technical conditions, as well as road studies of the kinematic parameters of the M1 category vehicle. As a result, a road method for monitoring the technical condition of shock absorbers was developed, taking into account their influence on stability indicators.
 Purpose – Theoretical substantiation and experimental confirmation of the method for diagnosing suspension systems by parameters related to the stability of the vehicle movement.
 Methodology includes experimental methods, mathematical modeling methods and numerical methods for solving differential equations.
 Results: experimental methods and mathematical apparatus were developed to study the influence of the parameters of the suspension system on the stability of the movement of vehicles.
 Practical implications: the results can be used by organizations and institutions involved in the development of diagnostic methods for vehicles.

Deep learning-based fault diagnostic network of high-speed train secondary suspension systems for immunity to track irregularities and wheel wear

Fault detection and isolation of high-speed train suspension systems is of critical importance to guarantee train running safety. Firstly, the existing methods concerning fault detection or isolation of train suspension systems are briefly reviewed and divided into two categories, i.e., model-based and data-driven approaches. The advantages and disadvantages of these two categories of approaches are briefly summarized. Secondly, a 1D convolution network-based fault diagnostic method for high-speed train suspension systems is designed. To improve the robustness of the method, a Gaussian white noise strategy (GWN-strategy) for immunity to track irregularities and an edge sample training strategy (EST-strategy) for immunity to wheel wear are proposed. The whole network is called GWN-EST-1DCNN method. Thirdly, to show the performance of this method, a multibody dynamics simulation model of a high-speed train is built to generate the lateral acceleration of a bogie frame corresponding to different track irregularities, wheel profiles, and secondary suspension faults. The simulated signals are then inputted into the diagnostic network, and the results show the correctness and superiority of the GWN-EST-1DCNN method. Finally, the 1DCNN method is further validated using tracking data of a CRH3 train running on a high-speed railway line.

Fault diagnosis of high-speed train suspension systems using multiscale permutation entropy and linear local tangent space alignment

Online health monitoring of railway vehicle suspension systems is of critical importance to guarantee train running safety. The currently reported works on vehicle suspension health monitoring mainly adopt model-based approaches. However, detailed parameters of the vehicle suspension systems, usually, are complicated to be acquired. Moreover, an accurate model cannot be easily obtained due to the nonlinearities of vehicle components and the complexity of suspension systems. Considering the limitations of the model-based approaches, a data-driven method, which combines multiscale permutation entropy and linear local tangent space alignment (MPE-LLTSA), is proposed to diagnose the faults of vehicle suspension systems. To demonstrate the effectiveness and advantages of this method, an MBS model of the China CRH3 train is built to generate the bogie frame’s lateral acceleration containing various secondary suspension faults, and the simulated signals are then introduced to evaluate the MPE-LLTSA method. The evaluation results show that the proposed data-driven approach can accurately identify different types of suspension faults. Finally, the MPE-LLTSA method is further validated using the tracking data of the CRH3 train running on a high-speed railway line in China, and the test results show that the proposed method has the potential to be applied to the field of railway engineering.

Correlation signal subset-based stochastic subspace identification for an online identification of railway vehicle suspension systems

ABSTRACT Monitoring the condition of suspension systems is significant to ensure the safe operation of modern railway vehicles. For this purpose, an online modal identification scheme, denoted as Correlation Subset based Stochastic Subspace Identification (CoS-SSI) is proposed in this paper to monitor the suspension conditions. Because of the widespread of the dynamic contact status between wheel and track, especially under faulty suspension cases, the vibration responses measured online exhibit high nonstationarity and nonlinearity. To take into account these characteristics of signals, the input correlation signals for SSI are clustered into several successive subsets according to their magnitudes, on which SSI is implemented one by one. In this way it yields a magnitude adaptive SSI for more reliable and accurate identification. Experimental studies were conducted on a 1/5th scaled roller rig system to verify the effectiveness of the proposed method for suspension monitoring. The experimental results show that the CoS-SSI outperform the conventional SSI in that it produces more reliable and realistic identification for the nonlinear system. Furthermore, the effectiveness of the CoS-SSI was verified experimentally with two faulty suspension faults induced into the system.

Failure Identification of Dump Truck Suspension Based on an Average Correlation Stochastic Subspace Identification Algorithm

This paper proposes a fault identification method based on an improved stochastic subspace modal identification algorithm to achieve high-performance fault identification of dump truck suspension. The sensitivity of modal parameters to suspension faults is evaluated, and a fault diagnosis method based on modal energy difference is established. The feasibility of the proposed method is validated by numerical simulation and full-scale vehicle tests. The result shows that the proposed average correlation signal based stochastic subspace identification (ACS-SSI) method can identify the fluctuation of vehicle modal parameters effectively with respect to different spring stiffness and damping ratio conditions, and then fault identification of the suspension system can be realized by the variation of the modal energy difference (MED).

Effects of Tyre Pressure on Vehicle Suspension Performance

Incorrect inflation pressures in tyres affects the vehicle handling, passenger comfort and braking conditions in addition to causing a reduction in fuel efficiency and tyre life. To address this problem, mathematical models have been produced and an experimental validation has been carried out. The models were developed with 7-DOF, for a full car system, using MATLAB programs. In the simulation study, the suspension faults have been considered by running the models with a range of inflation pressures at four conditions i.e. at standard pressure (2.3bar) and 1.5bar on the passenger wheel, driver wheel and front wheels. In each case, an analysis was carried out on the performances of the suspension in terms of ride comfort, road handling and stability of the vehicle followed by the presentation of the results obtained. In addition, the influence of parameter variations on transfer functions as a fault detection of suspension has been introduced. This approach has been used when detecting faults of vehicle tyres being under-inflated 35% and also to detect other suspension faults in the future.

Effects of Tyre Pressure on Vehicle Suspension Performance

Incorrect inflation pressures in tyres affects the vehicle handling, passenger comfort and braking conditions in addition to causing a reduction in fuel efficiency and tyre life. To address this problem, mathematical models have been produced and an experimental validation has been carried out. The models were developed with 7-DOF, for a full car system, using MATLAB programs. In the simulation study, the suspension faults have been considered by running the models with a range of inflation pressures at four conditions i.e. at standard pressure (2.3bar) and 1.5bar on the passenger wheel, driver wheel and front wheels. In each case, an analysis was carried out on the performances of the suspension in terms of ride comfort, road handling and stability of the vehicle followed by the presentation of the results obtained. In addition, the influence of parameter variations on transfer functions as a fault detection of suspension has been introduced. This approach has been used when detecting faults of vehicle tyres being under-inflated 35% and also to detect other suspension faults in the future.

Effects of Tyre Pressure on Vehicle Suspension Performance

Incorrect inflation pressures in tyres affects the vehicle handling, passenger comfort and braking conditions in addition to causing a reduction in fuel efficiency and tyre life. To address this problem, mathematical models have been produced and an experimental validation has been carried out. The models were developed with 7-DOF, for a full car system, using MATLAB programs. In the simulation study, the suspension faults have been considered by running the models with a range of inflation pressures at four conditions i.e. at standard pressure (2.3bar) and 1.5bar on the passenger wheel, driver wheel and front wheels. In each case, an analysis was carried out on the performances of the suspension in terms of ride comfort, road handling and stability of the vehicle followed by the presentation of the results obtained. In addition, the influence of parameter variations on transfer functions as a fault detection of suspension has been introduced. This approach has been used when detecting faults of vehicle tyres being under-inflated 35% and also to detect other suspension faults in the future.

Fault detection of vehicle suspension system using wavelet analysis

This paper presents a method based on continuous wavelet transform to detect the faults of vehicle suspension systems. The Morlet wavelet functions are employed to approach the natural frequencies of the system and the frequency components of the signal with relative maximum energy. To evaluate our method, we use a full vehicle dynamic model which has been simulated in ADAMS/CAR and validated by laboratory test results. The suspension faults have been considered due to the damage of shock absorbers (dampers) and upper damper bushings (UDBs) and assumed as the decrease in damping force and loose joints, respectively. In this paper, the incapability of the spectral analysis by using fast Fourier transform in analysis of the signals is revealed through applying the inputs that include transient characteristics and then wavelet transform employed to achieve more proper results. A swept frequency is applied as an input to the wheels that simulates the road irregularities. After detection of faulty sections of the system using signal energy distribution, the defects of damper and UDBs are distinguished from each other through observing the changes of natural frequencies and corresponding energy amplitudes.

Track and Vehicle Condition Monitoring during Normal Operation Using Reduced Sensor Sets

The Enhanced Rail Contribution by Increased Reliability (ERCIR) project aimed to enhance availability by condition monitoring of the track and vehicle suspension from in-service trains. In this project, the possibility of instrumenting an inservice train is explored, with emphasis on using a minimal sensor set for the detection and diagnosis of track and vehicle faults. The use of a bogie-mounted pitch rate gyro to observe mean vertical track geometry is novel, as is using a bogiemounted yaw rate gyro to observe mean lateral alignment as well as the more usual longer wavelength track curvature. Mathematical algorithms are developed to detect vehicle suspension faults and track irregularities. The suspension faults considered are changes in secondary lateral and anti-yaw dampers, and changes in effective conicity. Sudden changes are detected and diagnosed using a Kalman filter-based innovation approach; gradual changes in damping coefficients and effective conicity are detected by parameter estimation usin...