Design and Implementation of Big Data Cloud Platform Supporting

Fault Diagnosis and PHM System for Switch Equipment

Long Shi

1, a

, Rong Zhou

and Shulin Tan

Beijing National Railway Research & Design Institute of Signal & Communication Group Co. Ltd, South Road of

Automobile Museum, Beijing, China

Keywords: Switch equipment, PHM, fault diagnosis, cloud platform.

Abstract: Switch equipment plays an important role in railway signal system with the high failure rate. Ensuring the

good work condition of railway switch equipment is of great significance for the safety and efficiency of

railway transportation. Based on technologies such as information sensing, Internet of Things, cloud

computing, expert system, artificial intelligence and fault prediction and health management (PHM), a

structure design of the fault diagnosis and PHM system for switch equipment is put forward. A big data

cloud platform, gathering and managing massive data reflecting the working state of switch equipment, is

designed and implemented. It will be a powerful support for the system to reduce failure rate and improve

operation and maintenance capability of switch equipment.

1 INTRODUCTION

As an important railway signal equipment, railway

switch equipment is an indispensable part of

ensuring the safe operation of trains. At present, the

monitoring method of switch equipment relies on the

data of switch action current and voltage in the

centralized monitoring system of manual browsing,

which has the shortcomings of poor intelligence and

high leakage rate (Su K.Y., et al, 2007). The major

railway bureaus in China generally adopt the mode

of periodic maintenance and post-fault maintenance

to maintain the switch equipment (Li N. and Dong

H.Y, 2013). The health status of the switch

equipment can not be obtained in time. The

maintainers who are not rich in experience can not

accurately judge the causes of the switch equipment

faults. It is difficult to form an effective maintenance

plan (Zhang X., Du X.S. and Liu C.Y, 2009).

In order to adapt to the new situation of railway

development, a system for the real-time monitoring

and fault diagnosis of the railway switch equipment

with modern technology needs to be established

(Gao C., Zhou W.X. and Zhang Y.B, 2016). It is a

general trend to improve the interconnection, data

sharing and intelligence level, eliminate data islands,

combine information sensing, artificial intelligence,

big data, cloud computing, Internet of Things and

other technologies. Build an intelligent switch

equipment fault diagnosis and PHM system to solve

the existing problems and meet the growing demand

is an important foundation for railway integrated

operation and maintenance platform.

2 STUCTURE DESIGN OF THE

FAULT DIAGNOSIS AND PHM

SYSTEM

2.1 System Description

The fault diagnosis and PHM system of switch

equipment is based on information sensing, wireless

communication for data transmission, building big

data cloud platform and configuring station

customer service terminal, combing fault diagnosis

and prediction algorithms, expert knowledge

database, artificial intelligence, etc.

Big data cloud platform provides data source and

serves as the basis for switch fault diagnosis and

PHM. Big data and cloud computing complement

each other. Data mining of big data relies on

distributed processing, distributed database, cloud

storage and virtualization technology of cloud

computing. Cloud platform is not limited by time

270

Shi, L., Zhou, R. and Tan, S.

Design and Implementation of Big Data Cloud Platform Supporting Fault Diagnosis and PHM System for Switch Equipment.

DOI: 10.5220/0008377302700273

In Proceedings of 5th International Conference on Vehicle, Mechanical and Electrical Engineering (ICVMEE 2019), pages 270-273

ISBN: 978-989-758-412-1

Figure 1. Structure of the fault diagnosis and PHM system for switch equipment.

and space, switch acquisition units transmit all kinds

of monitoring data to cloud platform through

wireless communication anytime. Large-scale data

management and calculation problems of switch

equipment are processed and solved.

PHM is a comprehensive subject related to the

study of systemic health management. It uses the

collected information to predict the system failure

effectively before it occurs through various

intelligent and self-learning algorithms and models

to assess the health status of the system. PHM is a

further expansion of built-in test and state

monitoring capabilities for complex systems

traditionally used. With the ability to predict future

faults of the system, it transforms traditional

condition monitoring into systematic health

condition management, the occurrence of faults can

be identified and managed. Then a reasonable

maintenance plan is planned to reduce the cost. The

maintainability, safety and reliability of the system

are improved, the condition-based maintenance and

self-support of the system are realized (Zeng S.K.,

Michael G.P. and Wu J,2005).

The application of PHM technology in switch

system and even the whole railway signal system

can describe the state of equipment through real-

time monitoring data. When the equipment is in a

healthy state, the system monitors continuously to

analyze the degradation types, define health level,

predict the possible future failure and formulate a

reasonable maintenance plan. When the equipment

is in failure state, the system sends alarm to identify

and position the failure, shortens fault diagnosis time

and improves diagnostic efficiency.

2.2 System Structure

The fault diagnosis and PHM system for switch

equipment collects various state parameters of

switch machine, external locking device, installation

device and switch environment online, monitors and

predicts the typical faults of switch equipment for a

long time, and forms a reliable fault diagnosis model,

which provides basis and decision-making reference

for routine maintenance. The system includes

sensing layer, data acquisition and transmission

layer, cloud platform layer and fault diagnosis layer.

The structure is shown in Figure 1.

Sensing layer consists of various sensors for

running and environment status information

collection of switch equipment, including camera,

displacement sensor, current transformer,

temperature sensor, conversion force sensor, oil

pressure sensor, liquid level sensor, vibration sensor,

humidity sensor and switch gap sensor.

Design and Implementation of Big Data Cloud Platform Supporting Fault Diagnosis and PHM System for Switch Equipment

271

Data acquisition and transmission layer is formed

by switch acquisition units with wireless

communication. An acquisition unit communicates

with sensors in the sensing layer to acquire switch

equipment data, and communicates with cloud

platform through wireless communication, such as

4G, Lora, NB-IoT, etc.

Cloud platform layer is responsible for real-time

data transmission, data display, storage, statistical

analysis, remote communication, condition

monitoring, fault handling, management and other

functions.

Fault diagnosis layer reflects the actual situation

of the switch equipment with the functions of alarm,

early warning, fault diagnosis, fault prediction,

operation log, maintenance suggestion, etc.

3 DESIGN AND IMPLEMENT OF

THE BIG DATA CLOUD

PLATFORM

3.1 Function Description

Real-time data transmission with switch acquisition

unit.

Data display, storage, management and analysis.

Remote control of all switch acquision units.

Communication status monitoring.

Fault handling and alarm.

Event processing and decision making.

Safety protection.

3.2 Software Structure

Cloud platform software consists of three parts: C/S,

B/S and database. As shown in Figure 2.

C/S of cloud platform is mainly used for

communication with switch acquisition unit. On the

one hand, the received data is stored in the database

for reading by B/S at the appropriate time; on the

Figure 2. Software structure of the cloud platform.

other hand, C/S sends user commands (operation

commands, switch acquisition unit parameter

configuration commands, etc.) to the switch

acquisition unit.

B/S of cloud platform processes user operations

and presenting system data and status to users.

The database is used to store data, including the

data received by C/S from the switch acquisition

unit, as well as the user's control instructions and

operation records. At the same time, the database

also plays the role of communication between B/S

and C/S. The control instructions of the user for the

switch acquisition unit are written to the database by

B/S, and then read and sent to the switch acquisition

unit by C/S.

3.3 C/S Design

Data source of C/S is used to interact with system

database. Database write and query services are

provided. The module encapsulates all the details of

accessing the database and provides transparent data

operation services for the upper layer.

Data source module of C/S is based on ADO.net

data access module. Strong data sets are used as data

manipulation intermediaries. The functions of the

data source module in the whole C/S section are

shown in Figure 3.

3.4 B/S Design

B/S of the cloud platform provides human-machine

interface for users to view the system status and data.

It is also responsible for interpreting the user's

operations, then the related operations are

transferred into commands and stored in the

database for C/S to read.

Figure 3. Data source module of C/S.

ICVMEE 2019 - 5th International Conference on Vehicle, Mechanical and Electrical Engineering

272

Figure 4. B/S network diagram.

The network part adopts the Web platform

provided by Microsoft IIS and uses ASP. NET as the

Web implementation method, as shown in Figure 4.

The server side runs Windows Server operating

system, which integrates IIS 6.0. The client side uses

IE and Firefox browsers, and the communicaiton

protocol between client and server is TCP/IP. clients

interact with the server by requesting ASPX web

pages. IIS receives the page request information

from client browser, locates the ASPX pages, and

sends the request information to ASP. NET module

for processing. ASP.NET module analyses ASPX

files, executes the server-side command code,

generates pure HTML documents, and returns them

to IIS. Finally, IIS returns HTML to the client

browser.

3.5 Safety Measures

Wireless VPN private network communication is

established, data is encrypted, and virtual servers are

replaced by physical independent cloud servers to

achieve physical isolation of data.

Clear the responsibility for security management,

check and strengthen the database system, server

operating system, application middleware and

system source code of cloud platform, improve the

overall safety of the system and ensure the normal

operation of the system.

Intrusion monitoring system is set up in the cloud

platform system to monitor the server operating

system, prevent virus from entering the server and

affect the cloud platform, prevent illegal personnel

from operating, and regularly analyze and check the

alarm information.

Strengthen the security of local network, design

the network structure rationally, devide the system

according to the importance of information, separate

the general server and the core server by logical

isolation, adopt higher security strategy in the

control process, set up corresponding access control

rules, and reduce the safety risk of the cloud

platform.

According to the actual needs, configure and

optimize the cloud platform system, deploy security

software on the cloud platform to protect the

relevant information of the cloud platform system.

Strengthen the internal audit work, establish and

improve the application of security audit platform to

ensure that illegal operations are tracked, traced and

evidenced.

4 CONCLUSIONS

The fault diagnosis and PHM system based on big

data cloud platform achieves Internet of Things of

switch equipment by information sensing,

distributed acquisition and wireless communication.

Multiple status data of switch equipment are

collected, stored and analyzed. based on PHM,

expert system and artificial intelligence and

combined with specific failure modes such as

external locking block, closure adjustment and

indication adjustment, a mathematical model to

characterize the corresponding relation between

switch equipment work state and fault modes can be

established. On one hand, the system can locate fault

position and diagnose fault causes, and guide on-site

maintenance. On the other hand, the health status of

switch equipment can be predicted, and the

condition-based predictive maintenance strategy can

be effectively executed.

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Gao C., Zhou W.X. and Zhang Y.B., 2016. Research

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Li N. and Dong H.Y., 2013. Research on Fault Diagnose

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Su K.Y., Chen H.Y. and Ouyang H.Z., 2007. Use

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