The MAIANDROS System for Random-Vibration-Based On-Board Railway Vehicle and Track Monitoring

A bird’s–eye overview of the innovative, on–board and Multi–Purpose, random vibration based MAIANDROS Condition Monitoring system for railway vehicles and infrastructure is presented. The system includes Modules for Suspension Monitoring (SM), Wheel Monitoring (WM), Track Monitoring (TM) for track segment condition characterization, Lateral Stability Monitoring (LSM), and Remaining Useful Life Estimation (RULE) for critical components such as wheels. It is based on Statistical Time Series type methods and proper decision making, and aims at overcoming various challenges of current systems while pushing their performance limits. Its unique advantages include high diagnostic performance, ability to detect early–stage (incipient) faults, robustness to varying Operating Conditions, early detection of the onset of hunting, operation with a minimal number of low–cost sensors, and minimal computational complexity for achieving real–time or almost real–time operation. Its high achievable performance is demonstrated via indicative assessments using a prototype system onboard an Athens Metro vehicle and Monte Carlo simulations with a SIMPACK based high–fidelity vehicle model.

Railway Track Loss-of-Stiffness Detection Using Bogie Filtered Displacement Data Measured on a Passing Train

This paper presents an innovative numerical framework for railway track monitoring using acceleration measurements from sensors installed on a passenger train. A numerical model including a 10 degrees of freedom train passing over a three-layer track is employed. The bogie filtered displacement (BFD) is obtained from the bogie vertical acceleration using a numerical integration method and a band-pass filter. The BFD is compared to the filtered track longitudinal profile and can be seen to contain the main features of the track profile. This is also experimentally confirmed using field measurements where an in-service Irish Rail train was instrumented using inertial sensors. The proposed algorithm is employed to find the BFDs from the bogie accelerations. A track level survey was also undertaken to validate the measurements. It is shown that the BFDs from several passes are in good agreement with the surveyed profile. Finally, the BFDs are numerically used to find track defects such as hanging sleepers. The mean of the BFDs obtained from two populations of train passes over a healthy and a damaged track are employed to detect the loss of stiffness at the subgrade layer. The effect of the train forward speed variation and measurement noise are also investigated.

Smart Railway Track and Crossing Gate Security System Based on IoT

The railway system is one of the most widely used modes of transportation due to its low cost. To keep the railway system running smoothly, continuous track monitoring is needed. These days, the railway system is manually supervised. As a result, there is a greater risk of disasters, such as fatalities, occurring as a result of human error while monitoring. The main problem with manual system monitoring is that it takes a long time to process all of the necessary data. Since railway tracks are built over thousands of miles, it is virtually impossible to manually control the device over such a longdistance. At railway crossings, a lot of accidents happen. Crossing gates are usually opened and closed after receiving direct input from the station. If there is a delay in obtaining information from the station, there is a risk of swearing incidents. The main goal of this research is to simplify and protect the railway system. The proposed system employs Force Sensitive Resistor (FSR) detectors for automatic side road crossing protection. Any type of breakage, as well as vibration, can be efficiently detected with a higher degree of precision using Light Dependent Resistor (LRR) and laser detectors. In the event of an unexpected situation, such as an accident, the GSM module will begin communicating via message with the nearest control room for assistance. Sonar sensors are often used for obstacle avoidance when something unexpectedly appears in front of the train. The Internet of Things (IoT) has been added to the system to allow it to be monitored from anywhere in the sphere. The Arduino UNO is a microcontroller that serves as the system's backbone. The framework has the potential to be extremely beneficial to our country's railway economic growth.

Optimized Railway Track Condition Monitoring and Derailment Prevention System Supported by Cloud Technology

This paper describes an easy way to monitor railway track abnormalities and update information on the track’s status to the cloud. Abnormalities present in railway tracks should be identified promptly and rectified to ensure safe and smooth travel. In this paper, a cloud-based track monitoring system (CTMS) is proposed for the monitoring of track conditions. The micro-electro mechanical systems (MEMS) accelerometers which are mounted in the axle are used to measure the railway track abnormality. The measured signal is optimized using the flower pollination optimization algorithm (FPOA). Because of signaling problems in the global positioning system (GPS), it is difficult to estimate the exact location of the abnormality in real time. A new method is introduced to overcome this problem. It provides the location of an abnormality even when the GPS signal is absent. The performance of the CTMS is compared with three different speed scenarios of the vehicle. The information about the abnormality on the track can be shared with other trains that pass through the same location so that the driver can reduce speed in that location to avoid derailment. Finally, an experimental setup was developed and the performance of CTMS is studied under four different irregularity cases.

An IOT based rail track condition monitoring and derailment prevention system

This paper proposes a new method for monitoring the irregularities in railway tracks by updating the status of the tracks in the cloud. The IoT based Railway Track Monitoring System (IoT-RMS) is proposed for monitoring the health of the railway track. The system identifies any abnormality in the tracks at an early stage. These abnormalities are rectified before they develop for smoother transportation. The micro electro mechanical system (MEMS) accelerometers are placed in the axle box for measuring the signal. It becomes hard to identify the exact location of abnormalities when the global positioning system (GPS) falters due to signalling issues. In this paper, a new hybrid method is proposed for locating irregularities on a track; even in the absence of a GPS signal. Pre-processing of the GPS signal is carried out effectively because the sensors used in IoT-RMS are capable of functioning in a high noise environment. The IoT-RMS updates the location of the abnormality in the cloud and shares it with other trains that will be passing through that location. As a result, the drivers of trains respond accordingly and avoid derailment. An experimental setup has been developed for a study of the performances for four different abnormal cases, and the result shows the effectiveness of the proposed system.

Vessel Trajectory Analysis in Designated Harbor Route Considering the Influence of External Forces

A vessel must navigate along designated routes within a harbor area to ensure navigation safety. The impact of strong currents is one of the most dangerous factors in coastal navigation. However, it is challenging to determine the deviation of a ship in advance from the ship’s position data in the case of a marine accident. In this study, to support the decision-making of ship navigators and vessel traffic service (VTS) operators in track monitoring tasks, tracks were classified according to the tidal stream, and the track distribution was analyzed according to the tidal current situations. Marine accident analysis was performed to investigate the tidal influence on ship tracks. Track data were collected for 12 months from a VTS center in Korea, and tidal information was collected through a meteorological observation buoy. Representative tracks were extracted from the track data using the support vector regression (SVR) seaway model. K-fold cross-validation and a grid search were performed to determine the optimal parameters. The ship tracks appeared in specific patterns according to the forces and directions of tidal currents, and specific deviation patterns were observed. This study is expected to contribute to the reduction of marine accidents by predicting ship trajectories according to the tidal situations in advance.

Application of Compressed Sensing in NB-IoT-Based Structural Health Monitoring of Rail Tracks

Ultrasonic guided-wave testing is one of the most widely used technology for Structural Health Monitoring (SHM) of rail tracks. Currently, cable is the main tool of signal transmission for guided wave-based track monitoring systems. The installation of cables can significantly increase the system cost and restrict the flexibility of system deployment. In recent years, the NB-IoT technology has been gradually appied to the field of SHM, it offers long-range wireless communication among a large-scale sensor networks at the cost of minimum construction and maintenance. One primary obstacle hindering the integration of NB-IoT and guided wave-based track monitoring system is that the limited channel bandwidth of NB-IoT leads to significant transmission delay when transmitting the ultrasonic guided-wave signal sampled at Nyquist rate. In this paper, a Compressed Sensing (CS) framework for NB-IoT based rail-track monitoring system is proposed. The proposed CS framework utilizes the sparsity feature of the ultrasonic lamb-wave signal to enable sub-Nyquist sampling and maintain the feature of the measured signal at a low compression rate. To validate the proposed CS framework, the propagation time of lamb-wave is selected as the performance metrics. The experimental results show that compared with the traditional sampling method, the propagation time of lamb wave in rail track can be accurately extracted when the sampling rate is set to 100kHz, therefore, the channel bandwidth of NB-IoT can meet the delay-free data transmission of a single ultrasonic sensor.

Aircraft noise level calculation during take-off

A mathematical model with 4 degree of freedom created in Matlab for aircraft departure trajectory is described in this article. As a reference aircraft a midsize commercial passenger aircraft similar to an Airbus A320 has been chosen. The aircraft is represented by the rigid body and the parameters of model are collected from Airbus and the simulated departure trajectory at the Munich airport is based on a Standard Instrumental Departure. A semi-empirical model of Stone for predicting the jet noise has been used. The proposed model is validated against 10 real flights obtained from aircraft noise and flight track monitoring system at Munich airport. The computed error between the real data and modelling is reported on. Obtained results are presented numerical and graphically. The observed effects of flight operational parameters affecting the aircraft noise emission level during take-off represent subjects of discussions in the paper.

Railway Track Monitoring Using Train Measurements: An Experimental Case Study

This paper investigates the use of drive-by train measurements for railway track monitoring. An in-service Irish Rail train was instrumented while using accelerometers and a global positioning system. The measurements were taken over two months and the train bogie accelerations from 60 passes on the Dublin-Belfast line were used for this study. A 6 km section of the line is the particular focus, where the maintenance measurements from a Track Recording Vehicle (TRV) were available. The Hilbert transform is used to obtain the instantaneous amplitudes of the acceleration signals. A new representation of the signal is proposed to show the signal energy level as a function of train location. It is shown that the forward speed of the train has a significant influence on the energy level of the signals. Therefore, a two-step speed correction is applied to the data. First, data from passes with forward speed below a certain limit are removed from the data set. Subsequently, a scaling factor is defined for the remaining signals and the energy levels of those signals are scaled while using online speed measurements. The scaled amplitudes are compared with the TRV data. It is shown that the energy levels of the signals match the TRV measurements very well.