JP2019093892A - Rail wavy wear detection device, and rail wavy wear detection method - Google Patents

Rail wavy wear detection device, and rail wavy wear detection method Download PDF

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JP2019093892A
JP2019093892A JP2017224804A JP2017224804A JP2019093892A JP 2019093892 A JP2019093892 A JP 2019093892A JP 2017224804 A JP2017224804 A JP 2017224804A JP 2017224804 A JP2017224804 A JP 2017224804A JP 2019093892 A JP2019093892 A JP 2019093892A
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pass filter
wear
axis data
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岡田　信之
Nobuyuki Okada
信之 岡田
崇宏 笹内
Takahiro Sasauchi
崇宏 笹内
拓也 大庭
Takuya Oba
拓也 大庭
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Nippon Sharyo Ltd
Central Japan Railway Co
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Central Japan Railway Co
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Abstract

To provide a rail wavy wear detection device of which detection accuracy is improved.SOLUTION: This rail wavy wear detection device comprises: an acceleration sensor 11 which is a measuring apparatus which measures at least one of a vibrational acceleration and a noise to a travel time of a railway vehicle 10 which travels on a rail 25, and acquires time base data D1; and a CPU 13a which is an arithmetic unit which performs high-pass filter processing, performs absolute value processing, and performs low-pass filter processing of the time base data D1 thereby making high frequency vibration data D2, and convers the high frequency vibration data D2 into space axis data D3.SELECTED DRAWING: Figure 3

Description

本発明は、レール波状摩耗検出装置において、波状摩耗の発生の検知精度を高める技術に関する。   The present invention relates to a technology for enhancing detection accuracy of occurrence of wave wear in a rail wave wear detection device.

従来、鉄道車両を運行するにあたって、レールの摩耗によって車両に振動が発生してしまうような問題があった。この一因としてレール波状摩耗と呼ばれる摩耗状態があり、このレール波状摩耗が生じたレールの上を車両が通過すると、大きな騒音や振動を発生することになる。特に都市部の鉄道や地下鉄などでは大きな騒音や振動が問題となるため、その適切な管理が求められている。また、レール波状摩耗は車輪からレールに伝わる荷重変動の増加にも繋がるため、早期に発見し対策することが求められている。   Heretofore, there has been a problem that when the rail car is operated, the wear of the rail causes the car to vibrate. One of the causes of this is a wear state called rail corrugation wear, and when the vehicle passes over a rail on which the rail corrugation wear occurs, loud noise and vibration are generated. Particularly in urban areas such as railways and subways, since the noise and vibration become a problem, appropriate management is required. Also, since rail-like wear leads to an increase in load fluctuation transmitted from the wheel to the rail, it is required to detect and take measures early.

こうしたことから、近年、レール波状摩耗の検出方法について様々な検討がなされてきた。しかしその対策の多くは、時間周波数軸上でフィルタ処理などの信号処理を行うものであった。また、レール上を走行する鉄道車両の騒音からレールの波状摩耗を検出する試みも行われている。しかしながら、実際の鉄道車両の運行時には、様々な周波数成分が含まれているため、検出精度の点で問題があった。   For these reasons, in recent years, various studies have been made on a method of detecting rail corrugation wear. However, many of the countermeasures have been to perform signal processing such as filtering on the time frequency axis. In addition, attempts have also been made to detect the wavy wear of the rail from the noise of a railway vehicle traveling on the rail. However, since various frequency components are contained at the time of operation of an actual railway vehicle, there was a problem in terms of detection accuracy.

特許文献１には、レール波状摩耗検出方法、及びレール波状摩耗検出システムに関する技術が開示されている。レール上を走行する鉄道車両上で鉄道車両の走行時間に対して振動加速度及び騒音の少なくとも一方を測定して時間軸データを生成する。そして、時間軸データを鉄道車両の走行距離に関連づけられた空間軸データに変換し、空間軸データを空間周波数軸上でバンドパスフィルタ処理をしてフィルタ処理データを生成する。そして、フィルタ処理データに基づいてレールの波状摩耗の発生区間を決定する。こうすることで、レール波状摩耗の検出精度の向上を図ることが可能である。   Patent Document 1 discloses a technique related to a rail wavy wear detection method and a rail wavy wear detection system. At least one of vibration acceleration and noise is measured with respect to traveling time of the railcar on a railcar traveling on a rail to generate time axis data. Then, the time axis data is converted into space axis data associated with the travel distance of the railway vehicle, and the space axis data is subjected to band pass filter processing on the space frequency axis to generate filter processing data. And the generation | occurrence | production area of the wavelike abrasion of a rail is determined based on filter processing data. By doing this, it is possible to improve the detection accuracy of the rail-like wear.

特開２０１２−２１７９０号公報Unexamined-Japanese-Patent No. 2012-21790

しかしながら、特許文献１に記載の技術であってもレール波状摩耗の波高値が低い場合、振動加速度を用いた方法では検出精度を高める事が困難である。レール波状摩耗はなるべく早期に発見し、対策を講じることが必要とされるが、レール波状摩耗の波高値が低い場合には、他の振動などに紛れ易く発見が遅れるケースがある。   However, even with the technique described in Patent Document 1, when the peak value of the rail-shaped wear is low, it is difficult to improve the detection accuracy by the method using the vibration acceleration. Although it is necessary to detect rail corrugation wear as early as possible and take measures, if the peak value of rail corrugation wear is low, there is a case where the detection is easily delayed due to other vibrations and the like.

そこで、本発明はこの様な課題を解決する為に、検出精度を高めたレール波状摩耗検出装置、及びレール波状摩耗検出方法の提供を行うことを目的とする。   Then, in order to solve such a subject, an object of this invention is to provide the rail-shaped wear detection apparatus which raised detection accuracy, and a rail-shaped wear detection method.

前記課題を解決するために、本発明の一態様によるレール波状摩耗検出装置は、以下のような特徴を有する。   In order to solve the above-mentioned subject, a rail corrugation wear detection device by one mode of the present invention has the following features.

（１）レール上を走行する鉄道車両の走行時間に対して振動加速度または騒音の少なくとも一方を測定して時間軸データを取得する測定装置と、前記時間軸データをハイパスフィルタ処理し、絶対値処理し、ローパスフィルタ処理して高周波振動データとし、前記高周波振動データを空間軸データに変換する演算装置と、を備えること、を特徴とする。 (1) A measuring apparatus for acquiring time axis data by measuring at least one of vibration acceleration or noise with respect to traveling time of a rail vehicle traveling on a rail, high pass filtering the time axis data, and absolute value processing And low-pass filter processing to generate high-frequency vibration data, and an arithmetic device for converting the high-frequency vibration data into spatial axis data.

上記（１）に記載の態様により、レール波状摩耗の検出精度を高める事ができる。これは、測定装置によって取得した時間軸データに対してハイパスフィルタ処理、絶対値処理、ローパスフィルタ処理を順に行うことで、時間軸データに含まれる高周波成分を抽出することが可能となり、波状摩耗の波高値が低い場合であってもレール波状摩耗の周期成分を検出しやすくなる。その結果、波状摩耗の検出性能を向上することに貢献出来る。   According to the aspect described in the above (1), the detection accuracy of the rail-shaped wear can be enhanced. This is because it is possible to extract high frequency components included in time axis data by sequentially performing high pass filter processing, absolute value processing, and low pass filter processing on time axis data acquired by the measuring device, and it is possible to Even when the peak value is low, it becomes easy to detect the periodic component of the rail-shaped wear. As a result, it can contribute to the improvement of the detection performance of the wavelike wear.

また、前記課題を解決するために、本発明の別の態様によるレール波状摩耗検出方法は、以下のような特徴を有する。   Moreover, in order to solve the said subject, the rail wavy wear detection method by another aspect of this invention has the following characteristics.

（２）レール上を走行する鉄道車両の走行時間に対して振動加速度または騒音の少なくとも一方を測定装置によって測定することで時間軸データを取得し、演算装置によって、前記時間軸データをハイパスフィルタ処理し、絶対値処理し、ローパスフィルタ処理して高周波振動データとし、前記高周波振動データを空間軸データに変換し、前記空間軸データに基づいてレールの波状摩耗の発生区間を決定すること、を特徴とする。 (2) Time axis data is acquired by measuring at least one of vibration acceleration or noise with respect to traveling time of a railway vehicle traveling on a rail, and high-pass filter processing of the time axis data by an arithmetic unit , Absolute value processing, low-pass filter processing to obtain high frequency vibration data, convert the high frequency vibration data into space axis data, and determine the occurrence section of the wave-like wear of the rail based on the space axis data. I assume.

上記（２）に記載の態様により、（１）に記載のレール波状摩耗検出装置と同様に、レール波状摩耗の検出精度を高める事ができる。   According to the aspect described in the above (2), the detection accuracy of the rail-shaped wear can be enhanced as in the case of the rail-shaped wear detection device described in (1).

本実施形態の、鉄道車両の模式断面図である。It is a schematic cross section of the rail car of this embodiment. 本実施形態の、レール波状摩耗検出装置の電気的構成を説明するブロック図である。It is a block diagram explaining an electric configuration of a rail corrugation wear detection device of this embodiment. 本実施形態の、データ処理の手順についてのフロー図である。It is a flowchart about the procedure of the data processing of this embodiment. 本実施形態の、データ処理手順を示すブロック図である。It is a block diagram which shows the data processing procedure of this embodiment. 本実施形態の、データ処理された信号のイメージを示すグラフである。It is a graph which shows the image of the data-processed signal of this embodiment. 従来技術の、バンドパスフィルタ処理をした信号のデータを示すグラフである。It is a graph which shows the data of the signal which carried out the band pass filter processing of prior art. 本実施形態の、信号処理したデータを示すグラフである。It is a graph which shows the signal-processed data of this embodiment.

まず、本発明の実施形態について図面を用いて説明を行う。図１に、本実施形態の、鉄道車両の模式断面図を示す。図２に、レール波状摩耗検出装置１００の電気的構成についてブロック図を示す。鉄道車両１０は、図１に示すように、乗客などを乗せるための車体２０と、空気バネ２１を介して車体２０を支える台車２３と、台車２３に軸バネ２４を介して設けられレール２５上を走るための車輪２２とを有する。   First, an embodiment of the present invention will be described using the drawings. FIG. 1 shows a schematic cross-sectional view of a railcar according to the present embodiment. FIG. 2 shows a block diagram of the electrical configuration of the rail-shaped wear detector 100. As shown in FIG. As shown in FIG. 1, the railcar 10 is provided on a rail 25 with a car body 20 for carrying passengers and the like, a cart 23 for supporting the car body 20 via an air spring 21, and a cart 23 via a shaft spring 24. And wheels 22 for running.

レール波状摩耗検出装置１００は、図２に示すように、加速度センサ１１と、マイク１２と、処理装置１３と、出力装置１４と、入出力ポート１５を有する。加速度センサ１１は、車体２０に設置されて振動加速度を測定するものである。マイク１２は、騒音計などを用いたレール２５と車輪２２の接触に起因する車内騒音を集音するものである。出力装置１４は、画像出力装置などを用いた乗務員に情報を伝達するためのものである。ただし、液晶パネルなどの画像出力装置に限定される必要は無く、例えばプリンタなどを用いて紙面に印刷するような出力装置でも良い。   As shown in FIG. 2, the rail-shaped wear detection device 100 includes an acceleration sensor 11, a microphone 12, a processing device 13, an output device 14, and an input / output port 15. The acceleration sensor 11 is installed on the vehicle body 20 to measure vibration acceleration. The microphone 12 collects in-vehicle noise caused by the contact between the rail 25 and the wheel 22 using a sound level meter or the like. The output device 14 is for transmitting information to a crew using an image output device or the like. However, the present invention is not limited to an image output device such as a liquid crystal panel, and may be an output device that prints on a sheet of paper using, for example, a printer.

処理装置１３は、プログラムを実行するためのＣＰＵ１３ａ、プログラムを記憶するためのＲＯＭ１３ｂ、各種データを一時的に記憶するＲＡＭ１３ｃを備えている。そして、データを中継する入出力ポート１５を介して、処理装置１３と、加速度センサ１１、マイク１２、出力装置１４が接続されている。   The processing device 13 includes a CPU 13a for executing a program, a ROM 13b for storing a program, and a RAM 13c for temporarily storing various data. The processing device 13, the acceleration sensor 11, the microphone 12, and the output device 14 are connected via an input / output port 15 that relays data.

図３に、データ処理の手順についてフロー図に示す。図４に、データ処理手順を表すブロック図を示す。車両から計測するデータは、加速度センサ１１から取得する振動加速度データだけでなく、マイク１２から取得する車内騒音データを用いても良い。両方のデータを用いるとより精度を上げられるので好ましいが、ここでは加速度センサ１１からのデータを用いる例を説明している。マイク１２からのデータであっても流れは同じである。   FIG. 3 is a flowchart showing the procedure of data processing. FIG. 4 is a block diagram showing a data processing procedure. As the data measured from the vehicle, not only the vibration acceleration data acquired from the acceleration sensor 11 but also the in-vehicle noise data acquired from the microphone 12 may be used. The use of both data is preferable because the accuracy can be further improved, but an example using data from the acceleration sensor 11 is described here. Even if it is the data from the microphone 12, the flow is the same.

Ｓ１１では、サンプリングデータ取得を行う。車体２０に設けられた加速度センサ１１から、車体２０に発生する振動加速度を取得する。データは処理装置１３に備えられたＲＡＭ１３ｃに保存される。得られたサンプリングデータは、鉄道車両１０の走行時間に対して発生する振動加速度を示した時間軸データＤ１となる。Ｓ１２では、ハイパスフィルタ処理を行う。取得した時間軸データＤ１は、処理装置１３のＣＰＵ１３ａによってハイパスフィルタ処理が行われ、ハイパスフィルタ処理データが得られる。   At S11, sampling data acquisition is performed. Vibration acceleration generated on the vehicle body 20 is acquired from the acceleration sensor 11 provided on the vehicle body 20. The data is stored in the RAM 13 c provided in the processing device 13. The obtained sampling data is time axis data D1 indicating the vibration acceleration generated with respect to the traveling time of the railway vehicle 10. At S12, high pass filter processing is performed. The acquired time axis data D1 is subjected to high-pass filter processing by the CPU 13a of the processing device 13 to obtain high-pass filter processed data.

Ｓ１３では、絶対値処理を行う。処理装置１３のＣＰＵ１３ａにて、ハイパスフィルタ処理されたハイパスフィルタ処理データに対して絶対値処理が行われ、絶対値処理データが得られる。Ｓ１４では、ローパスフィルタ処理を行う。処理装置１３のＣＰＵ１３ａにて、絶対値処理された絶対値処理データに対してローパスフィルタ処理が行われ、ローパスフィルタ処理データが得られる。この一連の処理が行われた結果、高周波振動データＤ２が得られる。   At S13, absolute value processing is performed. The CPU 13a of the processing device 13 performs absolute value processing on the high-pass filtered data that has been subjected to high-pass filtering to obtain absolute value processing data. In S14, low pass filter processing is performed. The CPU 13a of the processing device 13 performs low-pass filter processing on the absolute value processed data subjected to the absolute value processing to obtain low pass filtered data. As a result of performing this series of processing, high frequency vibration data D2 is obtained.

Ｓ１５では、空間軸データＤ３に変換を行う。上記処理が施されてＲＡＭ１３ｃに格納された高周波振動データＤ２を、処理装置１３のＣＰＵ１３ａにて空間軸データに変換する。Ｓ１６では、波状摩耗周期成分を抽出する。空間軸データＤ３を用いて波状摩耗周期を抽出することで、レール２５の波状摩耗の検出を行うことができる。そして処理を終了する。   At S15, the space axis data D3 is converted. The CPU 13a of the processing device 13 converts the high frequency vibration data D2 subjected to the above processing and stored in the RAM 13c into space axis data. In S16, a wave-like wear cycle component is extracted. By extracting the wave wear period using the space axis data D3, the wave wear of the rail 25 can be detected. Then the process ends.

なお、検出された波状摩耗周期は、車体２０内に備えられた出力装置１４に表示されて、時間軸データやＧＰＳ等を用いた位置データを参照してレール２５に発生した波状摩耗の位置を特定する。或いは、鉄道車両１０から外部にデータを転送して図示しない外部のデータセンターにて波状摩耗を特定しても良い。図３のフローに従って得られるデータに関してブロック図として説明したのが図４であり、時間軸データＤ１をハイパスフィルタＦ１で処理した後、ハイパスフィルタ処理データを得る。   The detected wave wear period is displayed on the output device 14 provided in the vehicle body 20, and the position of the wave wear generated on the rail 25 is referred to with reference to time axis data and position data using GPS or the like. Identify. Alternatively, data may be transferred from the railway vehicle 10 to the outside, and the wavy wear may be specified at an external data center (not shown). FIG. 4 is described as a block diagram with respect to data obtained according to the flow of FIG. 3, and high-pass filtered data is obtained after processing the time axis data D1 with the high pass filter F1.

そして、ハイパスフィルタ処理データを絶対値フィルタＦ２で処理した後、絶対値処理データを得る。そして、絶対値処理データをローパスフィルタＦ３で処理した後、ローパスフィルタ処理データを得る。ハイパスフィルタＦ１乃至ローパスフィルタＦ３の一連の処理により、高周波振動データＤ２が得られる。これを、空間軸データＤ３に変換する。これらの図４で示された処理はすべてＣＰＵ１３ａの内部で計算処理される。   Then, after the high-pass filtered data is processed by the absolute value filter F2, the absolute value processed data is obtained. Then, after the absolute value processed data is processed by the low pass filter F3, low pass filtered data is obtained. High frequency vibration data D2 is obtained by a series of processes of the high pass filter F1 to the low pass filter F3. This is converted into space axis data D3. The processes shown in FIG. 4 are all calculated in the CPU 13a.

本実施形態のレール波状摩耗検出装置１００は上記構成であるので、以下に説明するような作用及び効果を奏する。   Since the rail-shaped wear detection device 100 of the present embodiment is configured as described above, the operation and effects as described below can be achieved.

まず、本実施形態のレール波状摩耗検出装置１００によって波状摩耗の検出精度の向上を図ることができる。これは、本実施形態のレール波状摩耗検出装置１００が、レール２５上を走行する鉄道車両１０の走行時間に対して振動加速度または騒音の少なくとも一方を測定して時間軸データＤ１を取得する測定装置である加速度センサ１１またはマイク１２と、時間軸データＤ１をハイパスフィルタＦ１＋絶対値フィルタＦ２＋ローパスフィルタＦ３で処理して高周波振動データＤ２を得て、高周波振動データＤ２を空間軸データＤ３に変換する演算装置であるＣＰＵ１３ａと、を備えているためである。   First, the detection accuracy of the wave wear can be improved by the rail wave wear detection device 100 according to the present embodiment. This is a measuring device that measures time acceleration data D1 by measuring at least one of vibration acceleration or noise with respect to the traveling time of the railway vehicle 10 traveling on the rail 25. Calculation of processing the acceleration sensor 11 or the microphone 12 and the time axis data D1 with the high pass filter F1 + the absolute value filter F2 + the low pass filter F3 to obtain the high frequency vibration data D2 and convert the high frequency vibration data D2 into the spatial axis data D3 It is because it is equipped with CPU13a which is an apparatus.

また、レール２５上を走行する鉄道車両１０の走行時間に対して振動加速度を加速度センサ１１によって、または騒音をマイク１２によって測定することで時間軸データＤ１を取得し、ＣＰＵ１３ａによって、時間軸データＤ１をハイパスフィルタＦ１＋絶対値フィルタＦ２＋ローパスフィルタＦ３で処理して高周波振動データＤ２を得て、高周波振動データＤ２より空間軸データＤ３に変換し、前記空間軸データに基づいてレールの波状摩耗の発生区間を決定するレール波状摩耗検出方法を用いるためである。   The time axis data D1 is acquired by measuring vibration acceleration with the acceleration sensor 11 or noise with the microphone 12 with respect to the traveling time of the rail vehicle 10 traveling on the rail 25, and the CPU 13a acquires time axis data D1. Is processed by the high pass filter F1 + absolute value filter F2 + low pass filter F3 to obtain high frequency vibration data D2 and converted from the high frequency vibration data D2 into space axis data D3; generation section of wavelike wear of the rail based on the space axis data To use the rail-like wear detection method to determine

図５に、データ処理された信号のイメージをグラフに示す。上から生データである時間軸データＤ１の信号と、ハイパスフィルタＦ１及び絶対値フィルタＦ２で処理された信号と、ローパスフィルタＦ３で処理された信号と、空間軸データを示す信号をそれぞれグラフに示している。このような高周波成分の繰り返し周期抽出処理が行われることで、波状摩耗周期が視覚的に把握し易くなる。図６に、従来の方法で処理した実際の信号をグラフに示す。図７に、本実施形態の信号処理した信号をグラフに示す。縦軸が信号の振幅を示す正規化値を示し、横軸に走行距離を示している。この様に、実際に計測したデータにおいても、図６に示すような従来の振動加速度のデータをバンドパスフィルタ処理する方法と比べ、図７に示すように本実施形態の信号処理をしたものの方がハッキリとした波状摩耗区間を決定することができる。   An image of the data processed signal is shown graphically in FIG. The signals of time axis data D1, which are raw data from above, the signals processed by the high pass filter F1 and the absolute value filter F2, the signals processed by the low pass filter F3, and the signal indicating space axis data are shown in the graph. ing. By performing repetition cycle extraction processing of such a high frequency component, it becomes easy to grasp the wavy wear cycle visually. FIG. 6 graphically illustrates the actual signal processed in the conventional manner. FIG. 7 shows a graph of the signal processed in this embodiment. The vertical axis indicates the normalized value indicating the amplitude of the signal, and the horizontal axis indicates the travel distance. In this way, compared to the conventional method of performing band pass filter processing of data of vibration acceleration as shown in FIG. 6, even in the data actually measured, the signal processing of this embodiment as shown in FIG. It is possible to determine the undulating wear section clearly.

この様に、鉄道車両１０に本実施形態のレール波状摩耗検出装置１００を備えることで、レール２５の点検を日常的に行うことができ、レール波状摩耗の波高値が低い場合であっても波状摩耗の検出精度を向上させることが期待できるため、波状摩耗の早期発見に貢献することが可能である。このことは鉄道車両１０が生じる騒音や振動を低減する他、レール２５の劣化を抑制する効果も見込める。レール２５のメンテナンスの手間も波状摩耗を早期に発見できることで、低減することが可能となる。   As described above, by providing the rail wavy wear detection device 100 according to the present embodiment to the railway vehicle 10, inspection of the rails 25 can be performed on a daily basis, and even when the wave height value of the rail wavy wear is low Since the detection accuracy of wear can be expected to be improved, it is possible to contribute to early detection of wavy wear. This can reduce noise and vibration generated by the railway vehicle 10, and can also be expected to suppress deterioration of the rail 25. It also becomes possible to reduce the labor of maintenance of the rail 25 by being able to detect the wave wear at an early stage.

以上、本発明に係るレール波状摩耗検出装置１００の実施形態を説明したが、本発明はこれに限定されるわけではなく、その趣旨を逸脱しない範囲で様々な変更が可能である。   The embodiment of the rail-shaped wear detection device 100 according to the present invention has been described above, but the present invention is not limited to this, and various modifications can be made without departing from the scope of the present invention.

１０ 鉄道車両
１１ 加速度センサ
１２ マイク
１３ 処理装置
１３ａ ＣＰＵ
１３ｂ ＲＯＭ
１３ｃ ＲＡＭ
１４ 出力装置
１５ 入出力ポート
２０ 車体
２１ 空気バネ
２２ 車輪
２３ 台車
２４ 軸バネ
２５ レール
１００ レール波状摩耗検出装置
Ｄ１ 時間軸データ
Ｄ２ 高周波振動データ
Ｄ３ 空間軸データ
Ｆ１ ハイパスフィルタ
Ｆ２ 絶対値フィルタ
Ｆ３ ローパスフィルタ 10 Rail Vehicle 11 Acceleration Sensor 12 Microphone 13 Processor 13a CPU
13b ROM
13c RAM
14 output device 15 input / output port 20 car body 21 air spring 22 wheel 23 carriage 24 axial spring 25 rail 100 rail wave wear detector D1 time axis data D2 high frequency vibration data D3 space axis data F1 high pass filter F2 absolute value filter F3 low pass filter

Claims (2)

レール上を走行する鉄道車両の走行時間に対して振動加速度または騒音の少なくとも一方を測定して時間軸データを取得する測定装置と、
前記時間軸データをハイパスフィルタ処理し、絶対値処理し、ローパスフィルタ処理して高周波振動データとし、前記高周波振動データを空間軸データに変換する演算装置と、を備えること、
を特徴とするレール波状摩耗検出装置。 A measuring device for measuring at least one of vibration acceleration and noise and acquiring time axis data with respect to traveling time of a railway vehicle traveling on a rail;
The time axis data is subjected to a high pass filter process, an absolute value process, and a low pass filter process to obtain high frequency vibration data, and an arithmetic unit for converting the high frequency vibration data to spatial axis data.
Rail-shaped wear detection device characterized by レール上を走行する鉄道車両の走行時間に対して振動加速度または騒音の少なくとも一方を測定装置によって測定することで時間軸データを取得し、
演算装置によって、
前記時間軸データをハイパスフィルタ処理し、絶対値処理し、ローパスフィルタ処理して高周波振動データとし、
前記高周波振動データを空間軸データに変換し、
前記空間軸データに基づいてレールの波状摩耗の発生区間を決定すること、
を特徴とするレール波状摩耗検出方法。 The time axis data is acquired by measuring at least one of vibration acceleration and noise with respect to the traveling time of the railway vehicle traveling on the rail by the measuring device,
Depending on the computing device
The time axis data is subjected to high pass filter processing, absolute value processing, and low pass filter processing to obtain high frequency vibration data,
Convert the high frequency vibration data into spatial axis data;
Determining an occurrence section of the wave-like wear of the rail based on the spatial axis data;
Rail-shaped wear detection method characterized by
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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110593025A (en) * 2019-10-22 2019-12-20 中铁二院工程集团有限责任公司 Dynamic vibration absorber for corrugation steel rail
CN110658005A (en) * 2019-10-08 2020-01-07 西南交通大学 Method for identifying rail corrugation diseases based on vehicle body acceleration
CN111637964A (en) * 2020-05-12 2020-09-08 西南交通大学 Rail corrugation identification method
CN111705558A (en) * 2020-05-15 2020-09-25 广州地铁设计研究院股份有限公司 System and method for realizing low-frequency vibration reduction by utilizing fastener spacing detuning
JP2021025210A (en) * 2019-07-31 2021-02-22 近畿車輌株式会社 Rail maintenance system and railway vehicle
CN112556627A (en) * 2020-11-20 2021-03-26 中国铁道科学研究院集团有限公司 Rail corrugation detection system and method
CN113218640A (en) * 2021-04-02 2021-08-06 铁科院(深圳)研究设计院有限公司 Vibration damping effect test method for vibration damping track
CN115140115A (en) * 2022-07-18 2022-10-04 中国铁道科学研究院集团有限公司 Sound and vibration combined high-speed railway rail corrugation identification method and device
CN115214735A (en) * 2022-07-25 2022-10-21 河南科技大学 Subway seat system for improving operation quality of rail transit

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH09269217A (en) * 1996-03-29 1997-10-14 Kawasaki Heavy Ind Ltd Abnormality detection method for railroad track and abnormality detection device
JP2000136988A (en) * 1998-10-30 2000-05-16 East Japan Railway Co Detection method for wave-shaped abrasion of rail
JP2005231427A (en) * 2004-02-18 2005-09-02 B M C:Kk Track monitoring device
JP2007145270A (en) * 2005-11-30 2007-06-14 Univ Nihon Track status analysis method, track status analysis device and track status analysis program
JP2012021790A (en) * 2010-07-12 2012-02-02 Railway Technical Research Institute Rail wavy abrasion detection method and rail wavy abrasion detection system
US20120199700A1 (en) * 2009-08-13 2012-08-09 Technische Universiteit Delft Method and Instrumentation for Detection of Rail Defects, in Particular Rail Top Defects
JP2016099118A (en) * 2014-11-18 2016-05-30 公益財団法人鉄道総合技術研究所 Bearing abnormality determination device, bearing abnormality determination system and bearing abnormality determination program

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH09269217A (en) * 1996-03-29 1997-10-14 Kawasaki Heavy Ind Ltd Abnormality detection method for railroad track and abnormality detection device
JP2000136988A (en) * 1998-10-30 2000-05-16 East Japan Railway Co Detection method for wave-shaped abrasion of rail
JP2005231427A (en) * 2004-02-18 2005-09-02 B M C:Kk Track monitoring device
JP2007145270A (en) * 2005-11-30 2007-06-14 Univ Nihon Track status analysis method, track status analysis device and track status analysis program
US20120199700A1 (en) * 2009-08-13 2012-08-09 Technische Universiteit Delft Method and Instrumentation for Detection of Rail Defects, in Particular Rail Top Defects
JP2012021790A (en) * 2010-07-12 2012-02-02 Railway Technical Research Institute Rail wavy abrasion detection method and rail wavy abrasion detection system
JP2016099118A (en) * 2014-11-18 2016-05-30 公益財団法人鉄道総合技術研究所 Bearing abnormality determination device, bearing abnormality determination system and bearing abnormality determination program

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2021025210A (en) * 2019-07-31 2021-02-22 近畿車輌株式会社 Rail maintenance system and railway vehicle
JP7025382B2 (en) 2019-07-31 2022-02-24 近畿車輌株式会社 Rail maintenance system and rail vehicle
CN110658005A (en) * 2019-10-08 2020-01-07 西南交通大学 Method for identifying rail corrugation diseases based on vehicle body acceleration
CN110593025B (en) * 2019-10-22 2024-03-26 中铁二院工程集团有限责任公司 Dynamic vibration absorber for wave-grinding steel rail
CN110593025A (en) * 2019-10-22 2019-12-20 中铁二院工程集团有限责任公司 Dynamic vibration absorber for corrugation steel rail
CN111637964A (en) * 2020-05-12 2020-09-08 西南交通大学 Rail corrugation identification method
CN111637964B (en) * 2020-05-12 2021-10-26 西南交通大学 Rail corrugation identification method
CN111705558A (en) * 2020-05-15 2020-09-25 广州地铁设计研究院股份有限公司 System and method for realizing low-frequency vibration reduction by utilizing fastener spacing detuning
CN112556627A (en) * 2020-11-20 2021-03-26 中国铁道科学研究院集团有限公司 Rail corrugation detection system and method
CN113218640A (en) * 2021-04-02 2021-08-06 铁科院(深圳)研究设计院有限公司 Vibration damping effect test method for vibration damping track
CN113218640B (en) * 2021-04-02 2022-08-16 铁科院(深圳)研究设计院有限公司 Vibration damping effect test method for vibration damping track
CN115140115A (en) * 2022-07-18 2022-10-04 中国铁道科学研究院集团有限公司 Sound and vibration combined high-speed railway rail corrugation identification method and device
CN115140115B (en) * 2022-07-18 2023-10-17 中国铁道科学研究院集团有限公司 Sound-vibration combined high-speed railway rail wave mill identification method and device
CN115214735A (en) * 2022-07-25 2022-10-21 河南科技大学 Subway seat system for improving operation quality of rail transit

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