RUNNING HEAD: Predictive Assessments PREDICTIVE POWER OF ...
Predictive Maintenance of Railway Points
-
Upload
sandeep-patalay -
Category
Documents
-
view
72 -
download
5
description
Transcript of Predictive Maintenance of Railway Points
From the desk of
Sandeep Patalay
Predictive Maintenance of Railway
Point Machines
Sandeep Patalay
Senior Systems Engineer, CMC Ltd
Abstract— The railway points (switches) are vital component of any Railway
Interlocking system. Regular maintenance of points is required to keep them in operating
condition. Present maintenance of points involves frequent inspection by maintenance
staff and is not fool proof. Currently Electronic Monitoring systems are available which
only logs the event and does not give any predictive analysis about the health of the
points subsystem. This paper discusses a new approach for maintenance and diagnosis of
railway points which is capable of remote monitoring and is intelligent enough to give
predictive maintenance reports about the railway point’s health. This reduces the effort
and huge costs in reducing manual monitoring and also it fool proof avoiding accidents.
Distributed data gathering and centralized data processing methods have been discussed
that not only report the fault but also give predictive measures to be taken by the field
staff to avoid catastrophic failures.
Introduction
Railways traverse through the length and breadth of our country covering 63,140 route
kms, comprising broad gauge (45,099 kms), meter gauge (14,776 kms) and narrow gauge
(3,265 kms). The most important part of the railways to carry out operations like safe
movement of trains and communications between different entities is Signalling. The
Railway signalling is governed by a concept called Interlocking. The main component of
the interlocking is the Railways Points consisting of DC electrical motors to switch the
rails to a different route. These vast and widespread assets to meet the growing traffic
needs of developing economy is no easy task and makes Indian Railways a complex
cybernetic system. The current mechanism in place to maintain the railway points are
completely manual and requires large pool of maintainers to check the validity of the
point machine and the related point infrastructure regularly, this process employed is
neither cost effective nor fool proof. By employing the traditional method of manual
maintenance, the rail operators do not have any prior warning for replacement or repair of
points. The discussion in this paper mainly focuses on development of a system that not
only monitors the points remotely without manual intervention, but also diagnosis the
problem in the point thus saving human lives and huge manual maintenance costs. The
motivation for developing a predictive maintenance system for Railway Points is as
follows:
1. To use an array of sensors to monitor all relevant parameters, in order to provide
advanced warning of degradation prior to railways points failure.
2. To provide predictive maintenance reports about the point machines to the
maintainers.
3. To provide continuous monitoring at both local and centralized locations.
4. To provide an automated archival record from which broad trends can be
extracted from the entire railway asset base.
5. To provide, in the event of a catastrophic failure, the immediate past history to
identify the cause.
Railway Points Structure
The following Figure 1 describes the architecture of railway points in operation.
Figure 1
Points, or switches as they are known, allow a rail vehicle to move from one set of rails to
another. They are a ‘digital output device’ in that there are only two acceptable states for
the point to be set in, ‘normal’, and ‘reverse’. Movement is carried out by way of a
geared motor, which actuates the stretcher bar. Location or state detection is made by
a two-position, polarized, magnetic stick contactor. A signal is fed back from these
switches to the signal box where all point directions are controlled and monitored. The
snap-action switches at the end of the stroke stop the machine and help brake the motor to
help reduce any impact at the end of the travel. Two stretcher bars (Figure 1) make sure
that the switch rails remain the correct distance apart – this can vary between installations
depending on the curvature of the main rails, and the speed limit of that section of the
track. There are usually two stretcher bars for each point machine. Any fault in this
mechanism like poorly securing of the bolts holding these stretcher bars, loose bolts etc.
may lead to deadly accidents.
Proposed Predictive Maintenance System Architecture
The proposed architecture of Predictive Maintenance System (PMS) for Railways points
is discussed below using the Figure 2
Figure 2 Architecture of PMS
Sensors are used to measure Voltage, Current, load and temperature of Point Motor. The
Throwing load sensor is used to measure the stress in the operating rod of the point
machine. The sensor values are read on real time basis by the wayside device and sent to
a central location for analysis. The wayside device uses GSM/GRPS network to transmit
this data to a central location. The Central Station analyzes the data in real time and
makes predictions on the point machines and stores them in to a database. The status of
any point machine can be viewed using any internet browser in the central station. The
Local station maintainers can view the data by logging in to the web server using any
internet browser. Based on the Current consumption, the load sensor values and the point
motor temperature, predictions are made for the maintenance or replacement of the Point
Motors. The central location is a Web server based architecture, where anyone with a
Web browser can login and see the details.
Data processing and analytics
The system has a database of current and load characteristics of good working railway
points. This data is used as a reference for processing real time data received from the
wayside units. The following figures show the current (i) and load sensor values plotted
against time during point machine operation.
Figure 3 Current Characteristics
Figure 4 Load Force Characteristics
Data Processing Techniques
Various Signal Processing Techniques are available for analysis of real time data
described below:
1. Data Cluster method – This involves recording the characteristics of a parameter
of a subsystem under different simulated conditions and then using this as a
reference to validate the real time data. This method is different from template
matching, since it not entirely based on matching the plotted characteristics.
2. Template matching – Entails comparing complete data sets with pre-recorded
examples of data resulting from known fault conditions. The method can be used
effectively in some circumstances, provided a representation of the data that
produces good discrimination between pattern classes can be made. However, this
requires a substantial amount of experimentation with different transformations of
the data sets to find such distinctions, and would be a computationally intensive
process.
3. Statistical and decision theoretic methods – Matches are made based on statistical
features of the signal. For example, the mean and peak-to-peak value are
evaluated for each vector, and plotted in feature space, whereby different patterns
are distinguishable because they form clusters for each class that are located apart
from the fully functioning case.
4. Structural or syntactic methods – Involves deconstructing a pattern or vector into
structural components, to enable comparisons to be made on more simple, sub-
segments of data rather than a complete vector. Mathematically, these methods
are similar to fractal-based compression routines.
The method that was of specific interest to this project was to use a data clustering
methodology where a database of good measurements as well as load sensor data
readings under various simulated faults in the laboratory on some specimen railway
points is stored and then the real time load sensor data is plotted against it. This
generates very unique clusters of data points which represent each type of fault.
By applying the above techniques, we get clusters of fault data. We have found that these
data clusters are unique in the sense that these represent different types of faults.
Figure 5 Force Data Clusters
Types of faults detectable
1. Tight lock on reverse side (sand on bearers both sides) – Refers to the lock which
holds the point in position after it has changed direction. This lock prevents the
point from moving out of position because of vibration.
2. A 12-mm obstruction at toe on normal side – Simulates a piece of ballast
impeding point motion between the toe of the switch rail (the mobile section of
rail), and the stock rail.
3. Back drive slackened off at toe end on LHS – The drive to the midpoint of the
switch rail is only loosely connected to the stretcher bar. The stretcher bar holds
the mobile rails a fixed distance apart.
4. Back drive slackened off at toe end on RHS – Similar to the above.
5. Back drive tightened at heel end on RHS – Similar to the above.
6. Back drive tightened at heel end on LHS – Similar to the above.
7. Diode snubbing block disconnected – An electrical fault.
8. Drive rod stretcher bar loose on RHS – Connecting bar between the switch rails is
loose. A dangerous fault.
9. Operational contact slackened off by four holes – Applies to the contact for
detecting when the point has completed motion.