Structural Health Monitoring of Train Coupling System

XiHong Jin1, David Zhang

2, Yongqiang He

1, Kevin Liu

2, Yanjun Zeng

1 and Gang Yan

1

1. CRRC Zhuzhou Locomotive Co, China, heyongqiang@crrczelc.cc

2. Broadsens Corporation, USA, David.zhang@broadsens.com

Abstract

A train coupler is a structure for connecting train cars in between. Structural failure of train

coupling system may cause accidents and even lead to catastrophic damages. Therefore, it is

critical to ensure that the couplers are in healthy structural condition. Cracks, corrosion and

metal fatigue are the most common structural failures for couplers. Currently, the inspections

of train coupler are performed offline during scheduled maintenance. Since about two thirds

of the coupler is hidden beneath the car body, it often requires the disassembly of the coupler

cover to perform the inspection. The inspection could be time consuming and labor intensive.

This paper introduces a Structural Health Monitoring (SHM) system for train couplers that

saves labor, improves efficiency, and increases inspection accuracy. The SHM system

consists of piezoelectric sensors that are permanently mounted on the train coupler, data

acquisition device and analysis software. The piezoelectric sensors will send and receive

ultrasound waves for the structural integrity inspection. The system is designed to perform

the online real-time inspection when the train is in service. The design of the system is

introduced, including sensor placement, data acquisition device and software. Initial testing

of the system shows that it can detect artificial damages successfully.

Keywords: Train coupler; Structural Health Monitoring

1. Introduction

A train coupler is a metal structure for connecting train cars in between. Each train car has

two couplers: one in the front and on in the back. The train coupler not only links the cars

together, but also absorbs shocks during braking. Modern train coupler system includes a

draft gear in the back to further take care of the compression and tension forces. The

mechanical properties of the coupler structure deteriorate over time due to metal fatigue,

heavy loading, exposure to environmental influences such as humidity, temperature, erosion

and corrosion. Structural failure of train coupling system may cause accidents and even lead

to catastrophic damages. The database established by the Federal Railroad Administration [1]

shows that train coupler failure is one of the top three train structure failures from the years

2004 to 2007 in the United States ((Figure 1, Federal Railroad Administration Office of

Safety Analysis 2008). Therefore, it is critical to ensure that the couplers are in healthy

structural condition by detecting the defects in time. Cracks, corrosion and metal fatigue are

the most common structural defects for train couplers.

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Figure 1 Accidents related to train structure failure

There have been extensive researches on the real-time monitoring of the train structures that

include wheels, axles, bogies, etc. [2-9]. However, there is not much work done on the

automatic monitoring of the train coupler. There are three reasons for the lack of monitoring

systems for the train coupler: The movement of the train coupler is so irregular and

complicated that vibration method is hard to detect any structural damages until it is too late;

the working environment of the train coupler is so demanding that sensors can be damaged

easily without proper protection; and the space of the sensor installation is very limited.

Currently, the inspections of train coupler are usually performed offline during scheduled

maintenance. Since about two thirds of the coupler is hidden beneath the car body, it often

requires the disassembly of the coupler cover to perform the inspection. The train needs to go

to an inspection station for such an operation. The inspection of train coupling system could

be time consuming and labor intensive. Therefore, it is desired to develop an automatic online

monitoring technology for the train coupling system.

The structure of a train coupler system is shown in Figure 2. There are multiple spots that are

subject to defects such as cracks, erosion or corrosion. Several key components that should be

monitored include the knuckle, coupler body, coupler yoke key area and the shank. This

paper introduces a Structural Health Monitoring (SHM) system for train couplers. A

piezoelectric sensor network is mounted on the train coupler to perform the real-time

monitoring of the structure status. The inspection can be done when the train is either on

service or offline. The objective of the train coupler SHM system is to save labor, improves

efficiency, and increases inspection accuracy.

The SHM system consists of a set of piezoelectric sensors that are permanently mounted on

the train coupler, a data acquisition device and analysis software. The piezoelectric sensors

are custom designed to withstand impacts and survive harsh environments. The sensors send

and receive ultrasound waves for the structural integrity inspection. The design and

installation of the sensors are introduced in section 2, followed by data acquisition device

design in section 3 and signal analysis software in section 4.

Figure 2 Train coupler structure

2. Sensor design and installation

Because the train coupler operates in a harsh environment, it is necessary to use sensors that

are resistant to environment factors such as wide temperature range and humidity range.

Moreover, the train coupler is subject to impacts by objects such as sands or small rocks.

Therefore, it is mandatory that the sensor is impact resistant. The system uses a compact

piezoelectric sensor made by Broadsens (BHU 100 sensor), as shown in Figure 3A. BHU100

sensor is designed for harsh environment with impact protection case. The sensor is also

water proof to work on rainy days. The BHU100 sensor has resonant frequency at 300kHz

and a capacitance of 1600pF at 1kHz. The BHU100 sensor has a default SMA connector on

one end for quick connection to the data acquisition system. Figure 3B shows various digital

sensors such as temperature sensor, acceleration sensor and sound sensor that can be used

with the BHU100 sensor. There are three digital sensors used in the system: A digital

temperature sensor is used for temperature compensation purpose, an accelerometer is used to

measure the movement of the coupler, and a digital strain sensor is used to measure the strain

on the coupler body. The case of the digital sensors are also custom made to withstand harsh

environment.

Because the piezoelectric sensors can work in the combination of pitch-catch and pulse-echo

mode, BUH100 sensors are mounted in pairs with a gap of 2mm in between. In pitch-catch

mode, one sensor in a pair will receive or send signals to the sensors in the different pairs. In

pulse-echo mode, one senor in a pair is used to transmit the excitation waveform, and the

other sensor in the same pair is used to receive the structural response. In the pulse-echo

mode, the time of flight information is used to help identify the location of the defects.

The sensors are bounded to the structure with epoxy. Installation of the sensors are made easy

with the hard-shell design. During the installation, the sensors were secured to the structure

with a magnet on top to allow the epoxy to cure. The wires of the BHU100 sensor is shielded

against EMI/EMC and UV resistant for long-term operation.

Figure 3A. BHU100 Ultrasound sensor Figure 3B. Digital sensors

Multiple areas on the train coupler need to be monitored. Therefore, there are total seventeen

BHU100 sensor pairs are used. Figure 4 shows the BHU100 sensors that are mounted on the

train coupler. The wires are further secured with clips bounded to the structure.

Figure 4 Ultrasound sensors mounted on the train coupler

3. Data acquisition and control software

The data acquisition system uses BroadScan D100 series multifunction ultrasonic scanner.

Most data acquisition devices for the Structural Health Monitoring uses single-ended input

method, where different channels share the same common ground. There exist strong

EMI/EMC noises when the train is in service. To minimize the environment noise,

BroadScan D100 has a unique differential-input design, where each channel has individual

signal and ground connection. The differential-input design of BroadScan D100 improves its

signal to noise ratio to be more than 80dB in offline test. BroadScan D100 can also connect to

up to 128 digital sensors to measure temperature, acceleration, humidity, pressure, sound, etc.

Digital sensors can be hot-plugged to the device and automatically found by the device.

For the train coupler monitoring, a customized BroadScan D100 data acquisition device is

designed to support seventeen sensor pairs. The data acquisition system communicates with

the server in the control room via Ethernet interface. A backup battery is built inside the data

acquisition system, so that the system can inspect the train coupler even there is no power

source from the train. The device is mounted on an area that is close to the train coupler.

Figure 5 BroadScan D100 data acquisition device

The control command and data are transmitted via HTTP protocol. User can log in to the data

acquisition system via a standard web interface to monitor the status of multiple sensors in

real time. One can configure the data acquisition parameters such as the sampling rate, the

signal path (excitation or receive channels), excitation voltage level and excitation signal

frequency. Figure 6 shows the software interface to monitor the time-history curve of

multiple digital sensors including temperate, humidity and acceleration. One can add or

remove a digital sensor from the control interface in real time. The sensor output time history

can be adjusted to show data in an hour, a day, a week or a month.

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Figure 6 Software interface to monitor sensors

Alternatively, ultrasound scan data can be stored in the data acquisition device. Later on, the

data can be downloaded via UDP interface. This is useful when user wants to schedule

periodic scan even when the train is without power source. UDP data transfer was tested

working well with the train software interface. The default size of data storage at the data

acquisition device is 64GB. Data storage can be extended to 256GB.

4. Signal analysis

To perform damage detection, a set of baseline data is collected when the train coupler is in

healthy condition. Then new data is compared with the baseline data. The difference between

the new data and baseline data is calculated via formula (1).

�� =

[� � − �(�)]����

�

�(�)��

�

(1)

Where �� is the damage index in a specified window, S is the starting time of the window to

evaluate the signal, T is the ending time of the window to evaluate the signal, y(t) is the new

data, x(t) is the baseline data. S and T are determined based on the wave speed and the area to

be monitored. The time window should be large enough to cover the monitored area and also

small enough to reduce the interference from other areas.

To compensate for the temperature effect, baseline data at different temperatures are

collected. Each baseline data has a temperature measurement associated with it. When the

new data is collected, then the new data is compared to the baseline data at the closest

temperature level.

A threshold value is used to determine if there is a damage or not. When �� is larger than the

threshold, then the software thinks that there is a potential damage. The threshold value is

related to the damage size that one is interested in. To detect a small damage, then a small

threshold value should be given. On the other hand, the threshold value should be selected so

that it is larger than the environment noise to avoid false alarm.

If a large number of sensors are used, then the damage location can be found with a mesh

network using the pitch-catch mode only. When there is a damage on the direct path of the

pitch-catch mode, then the signal of the direct path will be affected the most. The signal on

paths farther away from the damage will be affected less. By checking out which paths are

affected, and how much the data is affected, the location and the size of the damage can be

estimated with methods such as Reconstruction Algorithm for Probabilistic Inspection of

Defects [10].

In this paper, to reduce the number of sensors, damage location is identified by using the

combination of pitch-catch and pulse-echo mode. First, the pitch-catch mode identifies the

rough region of the defect. If there is a damage on the direct path or the adjacent area of the

pitch-catch mode sensor pair, then the signal will be affected. Then the damage location is

improved by using the pulse-echo mode. The sensor pair uses time of flight information to

identify the distance of the defect from the pair. The exact location of the defect can be

identified with the trilateration algorithm using multiple sensor pairs [11,12]. A minimum

three sensor pairs are required to find the damage location.

Damage is simulated with magnets of different sizes attached to the structure. To help the

ultrasound wave go through the magnet, the magnet’s flat surface was covered with

ultrasound gel. Figure 7 shows a 2cm diameter magnet attached to the steel structure.

Figure 7 Simulated damage with 2-cm diameter magnate

Figure 8 shows that in the pulse-echo mode, the baseline data without the magnet is

compared to the new data when the magnet is attached. The signal starts changing

significantly at around 2,500 sample points. The time of change matches the round-trip travel

time from the sensor pair to the magnet.

Figure 8 Signal comparison in Pulse-echo mode

The severity of the damage is related to the value of the damage index ��. The bigger the

variance, the severer the damage. Real damage will be introduced to the structure in the next

step to calibrate the damage index value with the size of the damage.

The train coupler SHM system scans the structure in a predefined time period (such as every

2 minutes). Each scan and computation lasts less than 30 seconds. A damage index curve

based on the �� is used to show the structure status in real time. Figure 9 shows the damage

index time history curve at one monitored area. When the signal variance exceeds the pre-

defined threshold, then the software issues an alarm. In Figure 9, a simulated damage made

with 2cm magnet was created around 8:10am, where the curve shows the abrupt jump of the

damage index. An alarm was sent to the control room when the simulated damage happened.

Figure 9 Damage index time history plot

5. Conclusions

This paper introduces a structural health monitoring system for train couplers. The train

coupler SHM system includes a piezoelectric sensor network, data acquisition system and

analysis software. The piezoelectric sensor has a special hard case that protects it against

impacts and rain drops. The system connects to the train central control system via Ethernet

interface.

Initial offline testing was performed with simulated damage. The simulated damage can be

found effectively by the system. In the next step, real damages will be introduced to the train

coupler. The size of the damage will be calibrated to the signal variance of the sensors. Then

dynamic testing of the train coupler SHM system will be carried out on a test bed. Online

testing of the system is scheduled to the implemented in the near future too.

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