Technology News
TIOSH

May 1997

A Fault Detection Neural Network
for de Trolley System Protection

Objective

To prevent mine fires caused by undetected arcing faults on
coal mine de trolley systems by using computerized signature
analysis techniques that distinguish legitimate loads from arcing
faults.

Background

De trolley haulage systems move personnel, supplies, and coal
in approximately 50 US. mines. A suspended trolley line en-
ergized at 300 or 600 V de provides electrical power, and a system.
of stecl track serves as the return path. When roof falls and other
events force the trolley line down near the ground retum rail,
inductance inherent in the trolley system facilitates continued cur-
rent flow along an ionized path between the line and rail. This
releases a significant amount of energy in the arc and may damage
and/or ignile surrounding material. Conventional circuit breaker
systems cannot prevent this continued arcing because the mag-
nitudes of the currents involved may be significantly less than
typical breaker trip settings.

In 1980, the former U.S. Bureau of Mines demonstrated re-
search to detect arcing and other types of trolley faults. The sys-
tem required an oscillator to superimpose a 3-kHz signal in the
trolley line, a signal wire suspended parallel to the trolley line for
circuit breaker coordination, and a filtering system on locomotives
larger than 25 tons. Although the system functioned satisfactorily,
the coal industry did not adopt it because of the complexity and
cost of the hardware.

Today, the threai of trolley fault-induced fires still exists. The
National Institute for Occupational Safety and Health (NIOSH) has
sought solutions that would require minimal hardware maintenance
and be cost-effective. Using an artificial neural network (ANN)
based system to detect trolley faults would require no modification
of the trolley rectifier, line, feeder, or its vehicles, lessening main-
tenance concerns and costs. Further, all hardware used in the
development of this system is commercially available.

Approach

Developing a method to safely induce arcing faults on mine
trolley systems and to record and analyze the electrical signature,
or output, of a trolley rectifier was the key to a neural network de-
tection algorithm for the project. Early field tests led to the devel-
opnient of an induced fault test circuit (figure 1) that was simply
clamped onto the trolley system. After closing of the contactor, the
fuse wire melted away and clectrical current arced between the
metallic electrodes, creating the induced fault. A custom data ac-
quisition/analysis system digitally recorded field data and per-
formed the necessary analysis to distinguish between normal op-
eration and an arcing fault. The system included a ruggedized PC,
an eight-channel analog-to-digital converter for data collection,
instrumentation for electrical signal gain or attenuation and elec-
trical isolation, and low-pass filters.

NIOSH personnel visited several coal mines in Pennsylvania
and West Virginia to conduct tests of de trolley systems under
various operating conditions. These conditions consisted of traffic
present on the system at any given time, e.g,, assorted vehicle traf-
fic; pump activity; and heavy, intermediate, and minimal loading.
Each test recorded the line current and line-to-ground voltage sig-
nature at a tralicy rectifier and varied in length from several sec-
onds to as much as 45 s, depending on the existing conditions of
the trolley system. These tests represented the normal operation,
or no-fault, portion of the data collected. Similar tests with the in-
clusion of an induced arcing fault comprised the faulted data tests.
To ensure observation of a variety of possible test conditions, care-
fully selected sites both on the surface and underground and at
various distances from the trolley rectifier served as fault locations.

Data collected during early field visits were used to train the
ANN, the basis of the arcing fault detection scheme. After the ANN
was sufficiently trained and tested in the laboratory, field tests were
conducted using the ANN to monitor the de current signal of a
trolley rectifier. Early field tests showed that the current signal alone
provided sufficient data for the fault detection algorithm.

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How It Works

To explain the operation of the fault detection algorithm, it is
important to first describe the training process of the ANN. The
ANN leamed by randomly selecting patterns from a training data set,
calculated an outpul based on these patterns (+1 for normal
operation or - 1 for a fault), and compared the result with the desired
output. Error values calculated during this stage were used to modify
the network architecture to achieve a result closer to the desired
output. These iterations continued until the network performance
became acceptable. The input was then switched to the test set
where the network conducted a single pass to generate independent
error values. This tested the network on control data not used in
training and determined if the training set was memorized. A
network that memorizcs training data becomes too specific and will
not perform well on new patterns. Further, examining the network
output and its variances between the set values of =1 (normal or
fault) helped identify weak areas in the training set. Additional
training patterns were later added to help improve network accuracy

After training and testing the network in the laboratory, it was
converted into a C language module. This module was then incor-
porated into a fault detection program written to man on a PC
platform and was taken into the field for further testing. Field testing
included repeating the same type of tests run during the recording
sessions, but with the ANN detection algorithm “monitoring” the
trolley rectifier current signal. After several postprocessing steps, the
network classification (fault or no-fault) was outputted to the user via
the PC display (figure 2).

Results

NIOSH personnel conducted a series of field tests using the final
detection algorithm in its “monitoring mode.” The results were very
promising. Sixty-two monitoring passes, each of which comprised
multiple classifications of cither fault or no-fault, were conducted,
The network correctly classified the status of the trolley system
97.9% of the time. Of the 2.1% incorrect classifications, 1.9%

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Trolley conducty

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Figure 1.—Trolley system induced arcing fault test circuit.

were situations where the algorithm failed to detect a fault and 0.2%
were false alarms.

The network discussed here is rectifier-specific, i.e., it was
trained solely on patterns obtained from a single mine rectifier. ‘This
would mean that in deploying this network to mines, it would require
training for each individual mine system. ]Jowever, the research
shows that this technology is a viable solution to the problem of
detecting de trolley faults. A detection system could be implemented
using a self-contained microprocessor mounted on each rectifier
where it would monitor the de current, providing an additional
margin of safety for U.S. mine workers.

For More Information

Mine operators or others interested in this new technology should
contact:

Jeffrey Shawn Peterson, National Institute for Occupational Safety
and Health (NIOSH), Pittsburgh Research Laboratory, Cochrans Mill
Rd., P.O. Box 18070, Pittsburgh, PA 15236-0070, Phone: (412)
892-4299, Fax: (412) 892-4384, e-mail: jnp3@cdc.gov. Mention of
any company name or product does noi constitute endorsement by
the National Institute for Occupational Safety and Health.

To receive additional information about mining issues or other
occupational safety and health problems, call 1-800-35-
NIOSH (1-800-356-4674), or visit the NIOSH Home
Page on the World Wide Web at hitp://www.cde.gov/niosh’
homepage htm.

As of October 1996 the safety and health research functions
of the former U.S. Bureau of Mines are now located in the
National Institute for Occupational Safety and Health
(NIOSH).

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Figure 2.—Detection network output before, during, and after
an induced fault. Fault occurs during Region A of lower graph.