Patent Description:
Although there is increasing demand to improve performance of maintenance of railway vehicles, frequent maintenance decreases train operation efficiency. Therefore, in the field of railway vehicles, a technical concept of carrying out maintenance at required timing, which is called CBM (Condition Based Maintenance), attracts attention for the purpose of, inter alia, curtailing downtime during which systems and services in operation will stop and reducing maintenance cost. In order to realize CBM, there is a need for an abnormality detection apparatus capable of early detecting abnormality regarding vibratory comfort and safety of railway vehicles and deciding on a cause of the abnormality.

As such an abnormality detection apparatus, for example, there is a technology disclosed in Patent Literature <NUM>.

Patent Literature <NUM> proposes an abnormality monitoring device which includes: an acceleration data acquisition unit for acquiring measurement data regarding the acceleration of a vehicle; an abnormality presence determination unit for determining the presence or absence of an abnormality on the basis of the comparison between the acceleration and a threshold value; a frequency analysis unit for analyzing the frequency of the acceleration in a case where the abnormality presence determination unit has determined the presence of an abnormality; and an abnormality type specification unit for specifying the type of an abnormality on the basis of the pattern of the frequency.

Patent Literature <NUM> proposes a method for the identification of poles and modal vectors of a road or rail vehicle, by means of the analysis of the movements or speeds or accelerations (output of the system) acquired in assigned measuring points of said vehicle, wherein said poles and modal vectors are determined by means of the fitting of the data relating to said outputs of the system on the basis of a mathematical model which describes the interaction between road or railway and said vehicle.

Patent Literature <NUM> proposes a railroad vehicle state monitoring device which includes: an amplitude ratio calculator for calculating an amplitude ratio between a vehicle body vibration acceleration and an axle box vibration acceleration of the railroad vehicle; a threshold determination processor for performing determination based on the amplitude ratio and a prescribed threshold; and a threshold determination processor for performing determination based on the axle box vibration acceleration and the prescribed threshold.

In Patent Literature <NUM>, disclosed is an abnormality detection apparatus that detects abnormality of a bogie, using acceleration of a bogie frame, which is acquired by an acceleration sensor provided in a bogie frame, and acceleration of a wheel axle, which is acquired by an acceleration sensor provided in a wheel axle. According to the abnormality detection apparatus disclosed in Patent Literature <NUM>, acceleration of a bogie is estimated with a physical model to which acceleration of a wheel axle acquired by an acceleration sensor is input and which outputs acceleration of a bogie in normal operation. Then, depending on whether or not estimated and measured values of the acceleration are close, judgment can be made as to whether or not abnormality occurs in the bogie.

Here, in the abnormality detection apparatus disclosed in Patent Literature <NUM>, to detect abnormality of a bogie, it is required to provide acceleration sensors respectively in both a wheel axle and a bogie frame with respect to one bogie. However, because larger vibration acceleration occurs in a wheel axle and a bogie frame than in a vehicle body, acceleration sensors having a high measurable range need to be used, which has brought about an increase in installation cost. Besides, because a wheel axle and a bogie frame have less space for installing an acceleration sensor as compared with a vehicle body, also, there has arisen a problem in which the degree of freedom of installing an acceleration sensor is limited. Furthermore, because a great number of acceleration sensors are required in the abnormality detection apparatus disclosed in Patent Literature <NUM>, also, there has arisen, inter alia, a problem in which time and cost for maintenance of these sensors increases.

The present invention has been developed in view of the problems noted above and is intended to provide an abnormality detection apparatus for rail vehicles capable of deciding on abnormality of what portion of a vehicle causing abnormal vibration of a vehicle, when occurring, based on information from sensors installed in vehicle bodies, while curbing costs for installation and maintenance.

To solve the problems noted above, claim <NUM> provides an abnormality detection apparatus for at least two rail vehicles pertaining to the present invention.

According to the present invention, it is possible to provide an abnormality detection apparatus for rail vehicles capable of deciding on abnormality of what portion of a vehicle causing abnormal vibration of a vehicle, when occurring, based on information from sensors installed in vehicle bodies, while curbing costs for installation and maintenance. Thereby, it is also enabled to curtail downtime of vehicles and reduce maintenance cost.

Problems, configurations, and advantageous effects other than described above will be made apparent from the following description of embodiments.

In the following, an abnormality detection apparatus for rail vehicles of the present invention will be described with reference to the drawings. Rail vehicles are vehicles that are operated to travel along rail tracks laid and include railway vehicles, monorail vehicles, streetcars, new transportation vehicles, etc. Railway vehicles are taken as a typical example of the rail vehicles and embodiments of the present invention are described.

First, a configuration of an abnormality detection apparatus for railway vehicles is described with reference to <FIG>.

<FIG> is a functional block diagram depicting a system configuration of the abnormality detection apparatus for railway vehicles.

Rail vehicles A and B (hereinafter referred to as vehicles A and B) that travel on a rail track <NUM> vehicle each have a vehicle body <NUM> and bogies <NUM>, respectively. The vehicle body <NUM> is mounted on the bogies <NUM> via air suspension spring <NUM>. A bogie <NUM> is comprised of, inter alia, a bogie frame <NUM>, an air suspension spring <NUM>, a yaw damper <NUM>, an axle box body <NUM>, a wheel axle <NUM>, an axle spring device <NUM>, and an axle box support rubber <NUM> which serves as a housing of bearings for the wheel axle <NUM>.

The wheel axle <NUM> is rotatably held with axle box bodies <NUM>, elastic suspension in a vertical direction is provided by the axle spring device <NUM> between an axle box body <NUM> and a bogie frame <NUM>, and elastic suspension in a transverse direction is provided by the axle box support rubber <NUM>. The air suspension springs <NUM> are disposed between the vehicle body <NUM> and the bogie frames <NUM> and, through these air suspension springs <NUM>, the vehicle body <NUM> is elastically supported by the bogie frames <NUM>.

On the floor of the vehicle body <NUM>, for example, a vibration data detection unit (a vibration data acquisition unit) <NUM> is installed to acquire data on vibration of the vehicle body <NUM>.

It should be noted that a physical quantity that the vibration data detection unit <NUM> detects to acquire data on vibration is not limited to acceleration; speed, displacement, distortion, sound, etc. may be detected. However, description in the present embodiment assumes that the vibration data detection unit <NUM> detects acceleration.

Besides, although the vibration data detection unit <NUM> is installed in the vehicle body <NUM> having relatively plenty of space for installation, the same may also be installed on, inter alia, a bogie frame <NUM> and an axle box body <NUM>, to enable it to detect vibration of these parts. Description in the present embodiment assumes that the vibration data detection unit <NUM> was installed in the vehicle body <NUM>.

Besides, although multiple vibration data detectors <NUM> may be installed in one vehicle, description in the present embodiment assumes that a single vibration data detection unit <NUM> was installed. Moreover, a position in which the vibration data detection unit <NUM> is installed may be a central position of the vehicle body <NUM>, a position directly above a bogie <NUM>, an end portion of the vehicle body <NUM>, such as a position where a vehicle equipment compartment is present.

Vibration data acquisition by the vibration data detection unit <NUM> is applicable in any of the following directions: in longitudinal, transverse, and vertical directions with respect to a traveling direction of the railway vehicle, translational (longitudinal translational, transverse translational, and vertical translational) directions and a rotational (roll pitch yaw) direction. Description in the present embodiment assumes that the vibration data detection unit <NUM> detects vibration in a transverse translational direction.

The vehicle body <NUM> is equipped with an operation data detection unit (an operation data acquisition unit) <NUM> having a function to acquire operation data such as traveling speed, a traveling position, and a vehicle occupancy rate from, e.g., an operational administrative system which administrates operation data.

Additionally, in <FIG>, an example is presented in which two rail vehicles (hereinafter simply referred to as vehicles) are each only equipped with the vibration data detection unit <NUM> and the operation data detection unit <NUM>; however, three or more multiple vehicles may be equipped with them.

Besides, the vibration data detection unit <NUM> and the operation data detection unit <NUM> may be provided in multiple vehicles in one train set or can also be provided in each vehicle in different train sets traveling on the same rail track.

An abnormality cause detection device <NUM> includes a data analysis unit <NUM>, a comparison processing unit <NUM>, a parameter estimation unit <NUM>, an abnormality cause decision unit <NUM>, and a decision result output unit <NUM> and these units constitute the abnormality detection apparatus to detect a cause of abnormal vibration of a railway vehicle. Additionally, the parameter estimation unit <NUM> and the abnormality cause decision unit <NUM> constitute a diagnosis unit.

Additionally, it is desirable to install the abnormality cause detection device <NUM> in a ground facility such as a rail yard where inspection and maintenance of vehicles are performed; however, that device may be installed as on-vehicle equipment.

The data analysis unit <NUM>, comparison processing unit <NUM>, parameter estimation unit <NUM>, abnormality cause decision unit <NUM>, and decision result output unit <NUM> are, for example, configured as those of a computing device to perform processing tasks, which will be described later, according to programs stored within the abnormality cause detection device <NUM>.

The data analysis unit <NUM> has a function to acquire, from multiple vehicles, vibration data detected by the vibration data detection unit <NUM> and operation data detected by the operation data detection unit <NUM>, distinguish whether a subject vehicle is placed in normal condition or abnormal condition, and output vibration data and operation data of the vehicles. A concrete processing flow that is performed by the data analysis unit <NUM> will be described later.

The comparison processing unit <NUM> has a function to acquire vibration data and operation data of the vehicles placed in either of abnormal and normal conditions, which has been output by the data analysis unit <NUM>, and output a result of comparison between a vehicle in abnormal condition and a vehicle in normal condition, based on the acquired data. A concrete processing flow that is performed by the comparison processing unit <NUM> will be described later.

The parameter estimation unit <NUM> has a function to estimate parameters regarding vehicle internal conditions from the result of comparison between a vehicle in abnormal condition and a vehicle in normal condition, which has been output by the comparison processing unit <NUM>.

Additionally, the parameters regarding a vehicle internal condition are those that are likely to be a cause of abnormality of vibration of a railway vehicle and refer to, for example, rigidity of an axle box support rubber <NUM> of a box support device installed in a railway vehicle, an attenuation coefficient of a yaw damper <NUM>, and a wheel thread inclination of a wheel axle <NUM> among others. A concrete processing flow that is performed by the parameter estimation unit <NUM> will be described later.

The abnormality cause decision unit <NUM> has a function to decide on a cause of abnormality from the estimated values of parameters which has been output by the parameter estimation unit <NUM>. A concrete processing flow that is performed by the abnormality cause decision unit <NUM> will be described later.

The decision result output unit <NUM> has a function to notify, inter alia, a driver of the vehicle body <NUM> and a ground operations manager or maintenance personnel of the cause of abnormality which has been output by the abnormality cause decision unit <NUM> via a monitor, a speaker, etc..

Then, using <FIG>, descriptions are provided about concrete processing flows by which the abnormality detection apparatus for railway vehicles of the present embodiment decides on a cause of abnormality.

Here, the processing flows for abnormality detection are described, using an example case where abnormal vibration has occurred, attributed to a cause that is a decrease in the attenuation coefficient because of oil leakage in the yaw damper <NUM> (see a vibration waveform b in <FIG>).

Additionally, description in the present embodiment assumes a case where an acceleration sensor in a transverse translational direction (hereinafter referred to as a transverse acceleration sensor) installed directly above the bogie's position was used as the vibration data detection unit <NUM>.

A vibration waveform a in <FIG> is a chart presenting vehicle body's transverse acceleration <NUM> measured by a transverse acceleration sensor installed in a vehicle A in place corresponding to the rail track position and a vibration waveform b in <FIG> is a chart presenting vehicle body's transverse acceleration <NUM> measured by a transverse acceleration sensor installed in a vehicle B in place corresponding to the rail track position. In the vibration waveforms a and b, it is seen that the higher the measured acceleration, the larger will be waveform amplitude.

With the assumption that yaw dampers <NUM> in the vehicle A are normal, when oil leakage has occurred in a yaw damper <NUM> in the vehicle B, attributed to a cause that is a decrease in the attenuation coefficient of the yaw damper, the amplitude of vehicle body's transverse acceleration of the vehicle B becomes larger, as compared with that of the vehicle A, and abnormal vibration occurs in the vehicle B. This condition is revealed by the fact that amplitude of the vibration waveform b is significantly higher than amplitude of the vibration waveform a (e.g., an average), as presented in <FIG>.

<FIG> is a flowchart to explain a processing procedure of the data analysis unit <NUM> in the abnormality detection apparatus for railway vehicles in <FIG>. Operation according to the flowchart in <FIG> is as described below.

First, at step S111, the data analysis unit <NUM> acquires vehicle body's transverse acceleration measurements from the transverse acceleration sensors in the multiple vehicles and simultaneously acquires operation data such as a traveling position, traveling speed, and a vehicle occupancy rate from the operation data detection unit <NUM>.

Then, at step S112, the data analysis unit <NUM> performs filtering on the vehicle body's transverse acceleration measurements acquired at step S111 using a commonly known filtering technique.

As the filtering, inter alia, preprocessing for analysis processing to be performed at step S113 is performed, such as processing to extract only a frequency band that is easily perceivable by human senses from the vehicle body's transverse acceleration measurements and processing to calculate a value of RMS (Root Mean Square) or a maximum of the acceleration measurements.

Moreover, at step S113, the data analysis unit <NUM> sets a traveling section or the like where vibration is especially large as a representative section from the filtered acceleration measurements and performs processing to extract vehicle body's transverse acceleration data and operation data such as traveling speed and a vehicle occupancy rate of each vehicle of, inter alia, the vehicles A and B in this representative section.

Through the processing at step S113, with regard to the vehicles A and B, vibration data in the same section can be extracted.

It should be noted that a representative section can be determined optionally, not limited to a traveling section where vibration is large. Also, there may be one representative section or two or more representative sections.

Additionally, as for a representative section, it is expedient to set a section for which, for each vehicle of, inter alia, the vehicles A and B, data pieces are present, measured under relatively similar measurement conditions including, inter alia, the following: traveling speed of each vehicle is equal or similar, the vehicle occupancy rates of each vehicle are similar, and measurement dates and time of each vehicle are close.

At step S114, the data analysis unit <NUM> compares vehicle body's transverse acceleration data pieces of the respective vehicles in the representative section and performs processing to distinguish whether each vehicle is placed in normal condition or abnormal condition. For distinguishing whether a vehicle is placed in normal condition or abnormal condition, a threshold based processing may be performed, i.e., a threshold of vehicle body's acceleration is set beforehand and a determination is made as to whether or not the threshold is exceeded; alternatively, a commonly known method may be used, i.e., distinguishing is performed using a method such as Malanobis-Taguchi System.

In the case of the present embodiment, through the threshold-based processing or the like, the vehicle B producing large vibration that exceeds a threshold <NUM> which is presented in <FIG> should be judged to be placed in "abnormal condition" and the vehicle A whose vibration does not exceed the threshold <NUM> in "normal condition".

At step S115, the data analysis unit <NUM> outputs the vehicle body's transverse acceleration data and operation data in the representative section, extracted at step S113, in addition to distinguishing information as to whether each vehicle is placed in "normal condition" or "abnormal condition" to the comparison processing unit <NUM>.

<FIG> is a flowchart to explain a processing procedure of the comparison processing unit <NUM> in the abnormality detection apparatus for railway vehicles in <FIG>. Operation according to the flowchart in <FIG> is as described below.

First, at step S211, the comparison processing unit <NUM> acquires the distinguishing information, inter alia, indicating that the vehicle A is placed in normal condition and the vehicle B is placed in abnormal condition and the vehicle body's transverse acceleration data and operation data in the representative section.

Then, at step S212, from the acquired vehicle body's transverse acceleration data, the comparison processing unit <NUM> calculates vehicle body's transverse acceleration PSD (Power Spectrum Density) with respect to vehicle body's transverse acceleration of each vehicle. The PSD is a spectral function that gives expression as a power value per unit frequency width (a width of <NUM>).

In the case of the present embodiment, for the vehicle A, the calculation gives a result as vehicle body's transverse acceleration PSD <NUM> of a vehicle placed in normal condition, as is presented in <FIG>; for the vehicle B, the calculation gives a result as vehicle body's transverse acceleration PSD <NUM> of a vehicle placed in abnormal condition, as is presented in <FIG>.

As presented in <FIG> and <FIG>, in a given frequency band <NUM>, the vehicle body's transverse acceleration PSD <NUM> of the vehicle B becomes larger than the vehicle body's transverse acceleration PSD <NUM> of the vehicle A.

At step S213, the comparison processing unit <NUM> a calculates a PSD ratio <NUM> that is a ratio between the vehicle body's transverse acceleration PSD <NUM> of the vehicle A placed in normal condition and the vehicle body's transverse acceleration PSD <NUM> of the vehicle B placed in abnormal condition, as in <FIG>.

This PSD ratio <NUM> indicates a frequency band in which PSD in abnormal condition differs from that in normal condition and a degree of the difference. Here, the PSD ratio <NUM> increases significantly in a frequency band <NUM>, as is evident from <FIG>.

Additionally, after ensuring that there is little influence of difference in response due to, inter alia, difference in rail track conditions between sections, because vibration data in the same section is extracted by the data analysis unit <NUM> beforehand, a change in response due to a change in transfer characteristics between the normal and abnormal conditions of the vehicles can be extracted as the PSD ratio <NUM>.

And now, an example where the PSD ratio <NUM> is used was described in steps S212 and S213. However, any value indicating a characteristic quantity regarding a change between the abnormal and normal conditions can be used instead of the PSD ratio; as an example, inter alia, a ratio between an RMS value of vehicle body's transverse acceleration in abnormal condition and that in normal condition can be used.

At step S214, the comparison processing unit <NUM> outputs this PSD ratio <NUM> to the parameter estimation unit <NUM>.

Next, a processing flow of the parameter estimation unit <NUM> is described with <FIG>. Operation according to the flowchart in <FIG> is as described below.

Additionally, description in the present embodiment assumes a case where the parameters to be estimated to identify a cause of abnormality are the spring constant of an axle box support rubber <NUM>, the attenuation coefficient of a yaw damper <NUM>, and the wheel thread inclination of a wheel axle <NUM>.

First, at step S311, the parameter estimation unit <NUM> acquires the PSD ratio <NUM> which has been output by the comparison processing unit <NUM>. This is referred to as the measured PSD ratio <NUM>.

Then, at step S312, using a dynamic model, the parameter estimation unit <NUM> calculates an analytic value of vehicle body's transverse acceleration PSD, when the parameters regarding vehicle internal conditions have values under normal condition.

This dynamic model is a model for predicting vibration characteristics of a railway vehicle. Diverse dynamic models can be used according to a type of vibration that one wants to predict, such as a vehicle transverse dynamic model to predict vehicle body's transverse vibration and a vehicle vertical dynamic model to predict vehicle body's vertical vibration, when a virtual vehicle in which the vehicle body <NUM>, bogies <NUM>, and wheel axles <NUM> are simulated with rigid bodies, axle box support rubbers <NUM>, yaw dampers <NUM>, and others are simulated with springs and dampers, and the respective rigid bodies are connected with the springs and dampers travels on a virtual rail track that simulates track irregularities, e.g., such as an alignment irregularity of a rail track <NUM> (a degree of irregularity of the lateral surfaces of rails in a longitudinal direction) and a gauge irregularity (an error in a fundamental dimension of an interval between left and right rails).

Additionally, to define a virtual rail track that simulates track irregularities, if there is actually measured track irregularity data, the track irregularity data may be used. However, if there is no track irregularity data measured, inter alia, a method may be adopted in which track irregularity data sets typical of track states (such as "good", "ordinary", and "poor" states) are prepared beforehand and a virtual rail track is defined by selecting and using one of these sets according to a train line or the like of a vehicle in service.

Besides, by using conditions such as traveling speed and a vehicle occupancy rate detected by the operation data detection unit <NUM> as the conditions for analysis with a dynamic model, estimation with high accuracy reflecting actual traveling conditions is possible.

In the present embodiment, with the parameters such as the spring constant of an axle box support rubber <NUM>, the attenuation coefficient of a yaw damper <NUM> and the wheel thread inclination of a wheel axle <NUM> being set to normal values, an analytic value of vehicle body's transverse acceleration PSD is calculated using a dynamic model.

Additionally, as for the normal values, a design value or an element test value is used for the spring constant of an axle box support rubber <NUM> and the attenuation coefficient of a yaw damper <NUM> and, inter alia, a value in new product condition or a control value in maintenance is used for the wheel thread inclination of a wheel axle <NUM>.

At step S313 and subsequent steps, the parameter estimation unit <NUM> estimates the parameters for the vehicle placed in abnormal condition.

At step S313, first, the parameter estimation unit <NUM> sets initially estimated values of the parameters. The initially estimated values of the parameters may be either normal values of the parameters or randomly generated values.

In the present embodiment, the parameter estimation unit <NUM> sets the initially estimated values of the spring constant of an axle box support rubber <NUM>, the attenuation coefficient of a yaw damper <NUM>, and the wheel thread inclination of a wheel axle <NUM> among others.

At step S314, the parameter estimation unit <NUM> estimates the parameters in abnormal condition using the measured PSD ratio <NUM> processed by the comparison processing unit <NUM>.

Using the initially estimated values of the parameters set at step S313, first, the parameter estimation unit <NUM> calculates an analytic value of vehicle body's transverse acceleration PSD with the parameters having the initially estimated values by using the dynamic model.

At step S314, then, the parameter estimation unit <NUM> calculates an analytic PSD ratio <NUM> that is a ratio between the analytic value of vehicle body's transverse acceleration PSD with the parameters having the initially estimated values and the analytic value of vehicle body's transverse acceleration PSD with the parameters having values under normal condition, calculated at step S312.

Then, the parameter estimation unit <NUM> determines estimated values of the parameters so that the analytic PSD ratio <NUM> will match the measured PSD ratio <NUM>, i.e., an error will be minimized, as is presented in <FIG> and <FIG>.

As an index of error evaluation, any index that makes it possible to evaluate the error can be used without restrictions; nevertheless, in the present embodiment, a value is used that is obtained by integrating difference between measured PSD ratio <NUM> and the analytic PSD ratio <NUM> per frequency of PSD ratio (this value will be referred to as an integrated difference value, hereinafter), as is presented in <FIG> and <FIG>.

In the present embodiment, the estimated values of the parameters are obtained to make matching between the measured PSD ratio <NUM> which was calculated and the analytic PSD ratio <NUM>, as is presented in <FIG>, with regard to the spring constant of an axle box support rubber <NUM>, the attenuation coefficient of a yaw damper <NUM>, and the wheel thread inclination of a wheel axle <NUM> among others.

<FIG> is a characteristic diagram with frequency being plotted on the abscissa and PSD ratio plotted on the ordinate, presenting the measured PSD ratio <NUM> represented in a sold line and the analytic PSD ratio <NUM> represented in a dotted line before parameter estimation and <FIG> is a characteristic diagram with frequency being plotted on the abscissa and PSD ratio plotted on the ordinate, presenting the measured PSD ratio <NUM> represented in a sold line and the analytic PSD ratio <NUM> represented in a dotted line after parameter estimation.

Before parameter estimation, as in <FIG>, a large difference between the measured and analytic values in some frequency band in which abnormal vibration has occurred; whereas, after parameter estimation, as in <FIG>, the measured and analytic values almost match with little difference.

After the termination of estimation, at step S315, the parameter estimation unit <NUM> outputs the estimated values of the parameters to the abnormality cause decision unit <NUM>.

And now, in the present embodiment, the description has been provided, assuming that one transverse acceleration sensor was installed in each vehicle body <NUM>. On the other hand, assuming a case where transverse acceleration sensors were installed in, inter alia, the vehicle body <NUM>, bogies <NUM>, and axle box bodies <NUM> respectively, by updating the parameters so that an analytic value will match multiple measured values of transverse acceleration detected from these sensors, it is possible to make the accuracy of parameter estimation higher.

Next, a processing flow of the abnormality cause decision unit <NUM> is described with <FIG>. Operation according to the flowchart in <FIG> is as described below.

First, at step S411, the abnormality cause decision unit <NUM> acquires the estimated values of the parameters which have been output by the parameter estimation unit <NUM>.

In the present embodiment, the abnormality cause decision unit <NUM> acquires the estimated values of the following: the spring constant of an axle box support rubber <NUM>, the attenuation coefficient of a yaw damper <NUM>, and the wheel thread inclination of a wheel axle <NUM>.

Then, at step S412, the abnormality cause decision unit <NUM> calculates a ratio between the estimated and normal values of each parameter.

Then, at step S413, the abnormality cause decision unit <NUM> compares this ratio with a preset threshold with respect to each parameter and judges a parameter as normal or abnormal.

In particular, for a parameter of which this ratio is less than or equal to the threshold, because it can be regarded as close to normal, the abnormality cause decision unit <NUM> judges the parameter as normal (step S414). On the other hand, for a parameter of which this ratio exceeds the threshold, the abnormality cause decision unit <NUM> judges the parameter as abnormal (step S415).

Then, at step S416, the abnormality cause decision unit <NUM> outputs a parameter judged as abnormal as a cause of abnormality to the decision result output unit <NUM>.

<FIG> is a schematic diagram which schematically represents an example of judging a parameter as normal or abnormal in step S413 and represents a relationship between the ratio between the estimated and normal values of a parameter and the threshold with regard to elements that may be a possible cause of abnormality, such as spring rigidity of an axle box support rubber <NUM>, the attenuation coefficient of a yaw damper <NUM> and the wheel thread inclination.

In the example of <FIG>, the attenuation coefficient of a yaw damper <NUM> is judged as abnormal, as the ratio between the estimated and normal values of this parameter exceeds the threshold <NUM>. Then, a result of decision that a decrease in the attenuation coefficient of a yaw damper <NUM> is a cause of abnormality is output from the abnormality cause decision unit <NUM> to the decision result output unit <NUM>.

Additionally, the larger the ratio between the estimated and normal values of a parameter, the parameter deviates from its normal value to a larger extent; therefore, the abnormality extent of the parameter can be evaluated on how large the ratio is.

The decision result output unit <NUM> notifies, inter alia, a vehicle driver and a ground operations manager or maintenance personnel of this result of estimating the cause of abnormality through the use of a commonly known communication technology.

As described hereinbefore, the abnormality detection apparatus for railway vehicles of the present embodiment is capable of estimating a cause of abnormality, like a decrease in the attenuation coefficient of a yaw damper <NUM>.

An abnormality detection apparatus for vehicles pertaining to the present embodiment has the same configuration as described for the foregoing first embodiment; however, its parameter estimation unit <NUM> performs processing which will be described below with a flowchart in <FIG>. And now, only details that differ from the first embodiment are mainly described below.

In the present embodiment, a step S312a is added as processing that is performed by the parameter estimation unit <NUM> described in the first embodiment without using track irregularity data prepared beforehand, such as measured data, with regard to track irregularity data which is used for analysis with a dynamic model.

It should be noted that processing steps that are the same as those described in the first embodiment are assigned identical reference signs and their detail description is omitted.

First, at step S311, the parameter estimation unit <NUM> acquires the measured PSD ratio <NUM> processed by the comparison processing unit <NUM>, as is the case for the first embodiment.

Then, at step S312a, the parameter estimation unit <NUM> performs processing to identify track irregularities, based on a measured value of vehicle body's transverse acceleration PSD of a vehicle placed in normal condition.

Here, the parameter estimation unit <NUM> performs the processing to identify track irregularity data so that the measured value of vehicle body's transverse acceleration PSD of the vehicle will match an analytic value of vehicle body's transverse acceleration PSD of the vehicle calculated with a dynamic model.

In the case of the present embodiment, track irregularity data is to be identified from vehicle body's transverse acceleration PSD of the vehicle A placed in normal condition.

In steps S313 thru S315 of processing, the parameter estimation unit <NUM> performs the same processing as in the first embodiment using the track irregularity data identified at step S312a.

It should be noted that, although an example of using vehicle body's transverse acceleration PSD was described for the present embodiment, no limitation to vehicle body's transverse acceleration PSD is intended, provided that track irregularity data can be identified.

As described hereinbefore, the abnormality detection apparatus for railway vehicles of the present embodiment is capable of deciding on a cause of abnormality without preparing track irregularity data beforehand.

In the abnormality detection apparatus of the present embodiment, step S314a and step S315a are added as processing that is performed by the parameter estimation unit <NUM> in a case where accuracy of estimating the parameters is low; these steps are added to the abnormality detection apparatus of the foregoing embodiments.

As described for the first embodiment, at step S314, parameter estimation unit <NUM> estimates the parameters so that an integrated difference value between the measured PSD ratio <NUM> and the analytic PSD ratio <NUM> of PSD ratio will be minimized. Here, the integrated difference value becomes an index indicating estimation accuracy.

At step S314a, if this integrated difference value is less than or equal to a preset threshold, the parameter estimation unit <NUM> judges that the estimation accuracy is more than or equal to a reference level and outputs the estimated values of the parameters to the abnormality cause decision unit <NUM> at step S315, as is the case for the first embodiment.

However, if the integrated difference value is not less than or equal to the preset threshold, the parameter estimation unit <NUM> judges that the estimation accuracy is lower than the reference level and outputs only information that the vehicle is abnormal, but does not output the estimated values of the parameters (including a parameter estimated to be a cause of abnormality).

As described hereinbefore, the abnormality detection apparatus for railway vehicles of the present embodiment is capable of outputting at least abnormality occurring in the vehicle without stopping abnormality detection, even in a case where the accuracy of estimating the parameters is not sufficient.

Claim 1:
An abnormality detection apparatus for at least two rail vehicles (A, B), comprising:
a vibration data acquisition unit (<NUM>) which acquires vibration data of the rail vehicles (A, B);
an operation data acquisition unit (<NUM>) which acquires operation data of the rail vehicles (A, B); and
a data analysis unit (<NUM>) which distinguishes whether each of the rail vehicles (A, B) traveling on a same track (<NUM>) is placed in normal or abnormal condition, based on the vibration data and the operation data, and extracts vibration data of the rail vehicles (A, B) in association with a normal or abnormal condition, characterised in that the apparatus further comprises:
a comparison processing unit (<NUM>) which compares the vibration data of a rail vehicle (A) placed in normal condition and the vibration data of a rail vehicle (B) placed in abnormal condition and calculates a result of comparison; and
a diagnosis unit which decides on a cause of abnormality of vibration in the rail vehicle (B) distinguished as being placed in abnormal condition, based on the result of comparison.