Description:TECHNICAL FIELD
[0001] The present disclosure relates to the field of locomotives. More particularly, the present disclosure pertains to a braking system in locomotives.

BACKGROUND
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Braking system plays an important role in any vehicular system, especially in locomotives due to their length and speed. A careful operation of brakes is needed for locomotives owing to different locomotive length, speed, track curvatures, and many other influential parameters. The application of brakes can be either by a centralized braking system in the locomotive or by the train driver. Brakes applied by the locomotive pilot depend on the current situation of the train movement, which impacts the evaluation of the braking efficiency based on the stopping distance as driver intervention will influence the calculated distance in terms of acceleration, deceleration, and other parameters pertaining to the real time situation.
[0004] Therefore, there is a need for improved techniques for overcoming the drawbacks in the prior art.

OBJECTS OF THE PRESENT DISCLOSURE
[0005] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as listed below.
[0006] It is an object of the present disclosure to evaluate a performance efficiency of different braking algorithms deployed on different locomotives.
[0007] It is an object of the present disclosure to implement a brake analyzer algorithm for computing an actual performance of braking system by segregating a first brake data obtained when a train’s braking system is applied from an another type of brake data applied by a locomotive pilot.
[0008] It is an object of the present disclosure to perform progressive data analysis on deceleration profiles of different standard braking algorithms in different locomotives.
[0009] It is an object of the present disclosure to suggest a best time of application of brakes in a particular locomotive for a safe halt considering various parameters like, but not limited to, the type of standard braking algorithm deployed in the locomotive, current speed, permissible travel distance without danger, algorithm’s nominal braking performance, calculated stopping distance along with the performance summary.

SUMMARY
[0010] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0011] In one aspect the present disclosure relates to a brake evaluator system for locomotives. The system includes a data wrangling server that processes raw data associated with a locomotive management system, wherein the raw data includes at least one of speed, brakes applied, absolute location, movement authority, data related to signaling types, signal direction, a current signal, and a next signal, a data storage server that stores data from the data wrangling server, a brake controller that evaluates the performance of one or more braking methods deployed in the locomotives and predicts a stopping distance to determine an optimal brake application time and a display unit that displays the performance efficiencies of different braking algorithms and also notifies the optimal time of brake application.
[0012] Further, the brake controller segregates a first brake data associated with a braking system from another type of brake data applied by a locomotive pilot to evaluate the performance. The data wrangling server includes a data collection component for collecting the raw data, a data cleansing component for detecting and correcting inaccuracies in the collected data, and a data transformation component for transforming the data to be compatible with the brake evaluator system.
[0013] In another aspect the present disclosure relates to a method for extracting brake data from a journey data. The method includes obtaining the journey data associated with each trip of a locomotive from a data storage server, filtering a locomotive data from the journey data, computing a speed gradient associated with the locomotivedata for any two successive time intervals, determining a change in the speed gradient based on a threshold level and storing the filtered data when the change in the speed gradient is less than the threshold level. Further the method includes filtering based on the locomotive data at a first time instance when a brake is applied in the locomotive to the locomotivedata at a second time instance until the impact of the brake is felt.
[0014] In one another aspect the present disclosure relates to a method for evaluating a brake performance. The method includes obtaining a pre-processed data from a database storage server, checking a speed data associated with the pre-processed data, assigning a speed category such as, low speed, medium speed or high speed to the pre-processed data based on the speed data and a speed threshold, calculating one or more performance factors for the pre-processed data to evaluate the brake performance, and storing the calculated performance factor in the data storage server. The method further includes a first performance factor based on an initial speed data, a final speed data and a time difference data and a second performance factor based on the initial speed data, the final speed data, the time difference data and a duration of applying brakes.

BRIEF DESCRIPTION OF DRAWINGS
[0015] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. The diagrams are for illustration only, which thus is not a limitation of the present disclosure.
[0016] FIG. 1 illustrates an exemplary network architecture (100) in which or with which a proposed braking system may be implemented, in accordance with an embodiment of the present disclosure.
[0017] FIG. 2 illustrates an exemplary block diagram (200) of the proposed braking system, in accordance with an embodiment of the present disclosure.
[0018] FIG. 3 illustrates an exemplary block diagram (300) of a data wrangling server, in accordance with an embodiment of the present disclosure.
[0019] FIG. 4 illustrates an exemplary block diagram (400) of a data storage server, in accordance with an embodiment of the present disclosure.
[0020] FIG. 5 illustrates an exemplary block diagram (500) of a brake controller, in accordance with an embodiment of the present disclosure.
[0021] FIG. 6 illustrates an exemplary flow chart (600) of a process of extracting brake data, in accordance with an embodiment of the present disclosure.
[0022] FIG. 7 illustrates an exemplary flow chart (700) describing the steps involved in brake performance evaluation, in accordance with an embodiment of the present disclosure.
[0023] FIG. 8 illustrates a flow chart (800) describing a procedure performed by a machine learning model, in accordance with an embodiment of the present disclosure.
[0024] FIG. 9 illustrates an exemplary computer system (900) in which or with which embodiments of the present disclosure may be implemented.
[0025] The foregoing shall be more apparent from the following more detailed description of the invention.

DETAILED DESCRIPTION
[0026] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
[0027] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[0028] The present disclosure relates to locomotives and more specifically to braking system in the locomotives. In an aspect of the present disclosure, the proposed system implements (1) a performance evaluator algorithm utilizing an amalgamation of methods to compute the efficiency of different braking algorithms, taking into account the variability in the performances of various brake algorithms as compared to computing efficiency by focusing only on fixed brake efficiency formula with known parameters, and (2) a stopping distance prediction algorithm for estimating time of applying brake. The stopping distance prediction algorithm considers various rail parameters like type of braking algorithm deployed in the locomotive, current speed, permissible travel distance without danger, algorithm’s nominal braking performance, calculated stopping distance, signaling aspects, and various other parameters to precisely compute and compare the efficiencies of the braking algorithms and estimate a time for applying the brake. The stopping distance prediction algorithm implements ensemble model with bagging and boosting to lower variance in prediction of the braking distance and enables lowering the bias and enhancing the model’s capacity to anticipate the outcomes.
[0029] In another aspect, the proposed system includes a brake performance evaluator for evaluating the performance efficiency of different braking algorithms deployed on separate locomotives and a brake controller implementing a brake analyzer algorithm for segregating a first brake data applied by a train’s braking system from another type of brake data applied by the locomotive pilot to compute an actual performance of the braking system thereby eradicating the effects caused by driver intervention. The first data is different from the second brake data. Removing the effects caused by driver intervention results in better comparison analysis and better decision making. Further, the brake analyzer algorithm performs progressive data analysis on deceleration profiles of different standard braking algorithms in different locomotives and suggests the best time of application of brakes in a particular locomotive for a safe halt considering various parameters like the type of standard braking algorithm deployed in the locomotive, current speed, permissible travel distance without danger, algorithm’s nominal braking performance, calculated stopping distance along with the performance summary of different braking algorithm deployed in different locomotives, etc.
[0030] Various embodiments of the present disclosure will be explained with reference to FIGs. 1-9.
[0031] FIG. 1 illustrates an exemplary network architecture (100) in which or with which a proposed braking system may be implemented, in accordance with an embodiment of the present disclosure. In FIG. 1, the exemplary network architecture (100) comprising stations (102), locomotives (104), a cloud storage (106) associated with a locomotive management system including the stations (102) and locomotives (104), a network (108), a brake evaluator system (110), and a network management system (112) are shown. In accordance with an embodiment of the present disclosure, stations (102) are train halts or stops where locomotives (104) are present. The locomotives (104) may be either stationary or in movement with respect to the stations (102). In some embodiments of the present disclosure, any one of the locomotives (104) may be approaching any one of the stations (102) and the brake evaluator system (110) may evaluate different braking algorithms to suggest a best time for applying the brake for the locomotive (104) for example, without limitations, while approaching the station (102). Here, the station or stations (102) and locomotive or locomotives (104) may be used interchangeably. The cloud storage (106) may store data related to different locomotives and stations and associated parameters such as, without limitations, type of braking algorithm deployed in a particular locomotive, current speed associated with the locomotive, a permissible travel distance without danger. The brake evaluator system (110) may then determine the nominal braking performance associated with the braking algorithm deployed in the particular locomotive, a pre-calculated stopping distance, and other related parameters based on the data obtained from the cloud storage (106).
[0032] Referring to FIG. 1, the brake evaluator system (110) includes a machine learning model for evaluating the efficiency of the different braking algorithms and predicting a stopping distance associated with the locomotives (104). The brake evaluator system (110) acts as a recommendation system for suggesting the best time of application of brakes in a particular locomotive for a safe halt considering various parameters such as, without limitations, the type of braking algorithm deployed in the particular locomotive, current speed associated with the particular locomotive, permissible travel distance without danger for the particular locomotive, nominal braking performance associated with the braking algorithm employed in the particular locomotive, and the pre-calculated stopping distance. Further, the brake evaluator system (110) provides a performance comparison chart associated with different standard algorithms to enable visualizing the best performing brake algorithm for different speed categories that aids in better decision-making in terms of deployment of specific algorithm in a locomotive. Stopping distance predictor shall assist in estimating the time of application of brake based on stored historical evidence.
[0033] Referring to FIG. 1, the network (108) may be any communication network. In an embodiment, the network (108) may include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network (108) may include, by way of example but not limitation, one or more of: a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a public-switched telephone network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, some combination thereof. The network (108) may be a communication network associated with a railroad network. Further, the network management system (112) comprises managing various components such as, without limitations, stations, rail track, locomotive, signals, etc.
[0034] A person of ordinary skill in the art will appreciate that the exemplary architecture (100) may be modular and flexible to accommodate any kind of changes in the architecture (100).
[0035] FIG. 2 illustrates an exemplary block diagram (200) of the proposed brake evaluator system (110), in accordance with an embodiment of the present disclosure.
[0036] In FIG. 2, various components included in the brake evaluator system (110) such as, but not limited to, a data wrangling server (202), a data storage server (204), a brake controller (206), and a local display unit (208) are shown. The brake evaluator system (110) evaluates the efficiency of the different braking algorithms deployed in locomotives (104) of FIG. 1 by performing extensive data analysis and braking pattern recognition. The brake evaluator system (110) helps to predict the stopping distance and acts as a recommendation system for suggesting the optimal time for application of brakes for safe locomotive halt. The brake evaluator system (110) performs comparison analysis of the performance efficiencies of the different braking algorithms implemented in different locomotives and aids in better decision-making.
[0037] Referring to FIG. 2, the data wrangling server (DWS) (202) helps in collecting the raw data of locomotives, signals, tracks, and other rail parameters and pre-processes the collected data to transform to a form that may be used for processing by a computer system. Further, the data storage server (204) periodically stores data from the DWS (202). Furthermore, the brake controller (206) predicts a stopping distance to suggest an optimal time for applying the brake and the display unit (208) displays recommendation(s) related to an optimal brake application time to properly stop the locomotive at required places and also the efficiency comparison of different braking algorithms in a graphical format for better decision making with respect to the deployment of braking algorithms in locomotives and an efficiency comparison for different braking algorithms.. Further, the detailed working of the DWS (202), the data storage server (204), and the brake controller (206) are discussed below with reference to FIGs. 3, 4, and 5, respectively.
[0038] FIG. 3 illustrates a block diagram (300) of a data wrangling server (202), in accordance with an embodiment of the present disclosure.
[0039] In FIG. 3, the DWS (202) of FIG. 2 comprising various components such as, but not limited to, a locomotivedata collection component (302), a locodata cleansing component (304), and a locomotivedata transformation component (306) are shown. The locomotivedata collection component (302) of the DWS (202) collects the raw data associated with the locomotives (104) like speed, brakes applied, absolute location, movement authority, data related to signaling types, signal direction, current, next signal, etc. from the cloud storage (106) of locomotive management system so that data can be examined and understood for further processing. The data collected by the locomotivedata collection component (302) undergoes a cleansing process performed by the locomotivedata cleansing component (304). The locomotivedata cleansing component (304) detects and corrects the inaccurate, corrupted (e.g., null values), and irrelevant parts of the data (e.g., missing sequence) that may lead to misinterpretation and distortion of the results of analysis. Further, the data cleansed by the locomotivedata cleansing component (304) is sent to the locomotivedata transformation component (306) for transforming the data to a form that can be used by the brake evaluator system (110) for providing a recommendation related to applying brake.
[0040] FIG. 4 illustrates a block diagram (400) of a data storage server (204), in accordance with an embodiment of the present disclosure.
[0041] In FIG. 4, various components present in the data storage server (204) such as, without limitations, a historical data storage component (402), a brake pattern storage component (404), a result analysis storage component (406), and a model storage component (408) are shown. The historical data storage component (402) periodically stores the data obtained from the DWS (202). This data may be used for historical analysis to find meaningful insights and hidden data patterns related to the braking system. The brake pattern storage component (404) stores the braking data patterns generated by the application of brakes by different standard third party braking algorithms. Further, the result analysis storage component (406) stores the performance efficiency comparison analysis of the braking algorithms, and the model storage component (408) stores the stopping distance prediction model for predicting the stopping distance considering an initial speed.
[0042] FIG. 5 illustrates a block diagram (500) of a brake controller (206), in accordance with an embodiment of the present disclosure.
[0043] In FIG. 5, various components of the brake controller (206) such as, without limitations, an information retrieval component (502), a data analyzer component (504), a pattern learning component (506), and a brake analyzer component (508) are shown. The information retrieval component (502) fetches the locomotive movement data along with applied brakes, signals aspect ratio, movement authority, speed of locomotives, and the various brake modes from the data analyzer component (504) analyses the data to obtain better insights related to application of brake, the pattern learning component (506) learns patterns of brakes applied by different standard third party algorithms deployed in separate locomotives, and the brake analyzer component (508) evaluates the braking efficiency performance of the different algorithms for different speed categories.
[0044] In an embodiment, the brake analyzer component (508) performs the function of comparing performances of various braking models and predicting a stopping distance. For example, without limitations, a first part of the brake analyzer component (508) provides a performance comparison chart among different standard brake algorithms and a second part of the brake analyzer component (508) assists in estimating a time for applying the brake based on the historical evidence which provides better decision-making.
[0045] FIG. 6 illustrates a flow chart (600) describing the process of extracting brake data, in accordance with an embodiment of the present disclosure.
[0046] In FIG. 6, the method (600) describes a gradient analyzer algorithm utilized for brake pattern extraction. The method (600) includes obtaining a journey data at step 602, wherein the journey data is stored in the data storage server (204) of FIG. 2 from each trip of the locomotive on a daily basis. In an embodiment, the journey data includes a locomotive data such as speed, brakes applied, absolute location, movement authority, data related to signaling types, signal direction, current, next signal, etc.
[0047] Further, the method (600) includes filtering, at step 604, the locomotive data from the moment a first-time instance the brake is applied until the time, i.e., a second time instance when the impact of the brake is felt based on the journey data. At step 606, the method (600) includes computing the speed gradient across two successive time intervals, for example, T1 and T2, and examining, at step 608, for any sudden change in the gradient based on a threshold level and continuing to step 604 when there is a sudden change in the gradient i.e., when the computed speed gradient is more than a threshold. The sudden change in gradient indicates the presence of other external braking variables. On the other hand, when there is no sudden change in the gradient, i.e., when the change in gradient is less than the threshold, the method (600) includes storing, at step 610, the filtered data as a first result set in the data storage server (204) of FIG. 2.
[0048] FIG. 7 illustrates a flow chart (700) describing the steps involved in brake performance evaluation performed by the brake controller (206) of FIG. 2, in accordance with an embodiment of the present disclosure.
[0049] In FIG. 7, the method (700) describes calculating a performance factor associated with different braking algorithms. The method (700) includes obtaining, at step 702, a pre-processed data from the data storage server (204) of FIG. 2, wherein the pre-processed data is obtained by retrieving travel data and applying a variety of pre-processing techniques such as, without limitations, imputing missing values and standard normalization on the retrieved travel or journey data. The travel data includes the locomotive data such as speed, brakes applied, absolute location, movement authority, data related to signaling types, signal direction, and current and next signal, etc. The method (700) proceeds with checking a speed value or speed data associated with the pre-processed data at step 704, and assigning the speed category to be either low, high, or medium at steps 706, 708, 710, respectively based on a speed threshold. For example, in some embodiments, if a locomotive travels at a speed less than 30 Kmph, then it is categorized as low speed, if the speed is between 30 Kmph and 70 Kmph, it is categorized as medium speed, and if the speed is more than 70 Kmph, then it is categorized as high speed. For each of the speed categories, the braking system generates a variety of performance factors.
[0050] Referring to FIG. 7, upon categorizing the pre-processed data at steps 704, 706, 708, 710, the method (700) proceeds with calculating a performance factor 1 at step 712 and a performance factor 2 at step 714 and based on the two performance factors measures, a brake performance is evaluated. The performance factor 1 and performance factor 2 are given below as

Where, is difference between initial speed and final speed, is time difference between initial speed and final speed, and dt is the duration of brake applied. The initial speed refers to the speed of locomotive when brake was first applied, and final speed refers to the speed of locomotive till the last application of brake considering the gradient factor. Performance factor 1 is determined through multivariant analysis considering initial speed, final speed, and time difference i.e., the time taken from initial speed to final speed, while performance factor 2 is determined through multivariant analysis of the initial speed, final speed, time difference, and the kind and duration of brakes used.
[0051] Referring to FIG. 7, upon calculating the performance factors at steps 712 and 714, the method (700) proceeds with checking at step 716 whether the performance factor is calculated for all braking models and repeats the steps 712, 714 if the performance factor is not calculated for all the braking models. On the other hand, if the performance factor is calculated for all the braking models, the method (700) proceeds with storing, at step 718, the performance results as a second result set in the data storage server (204) of FIG. 2. Further, the first result set obtained by executing the method (600) and the second result set obtained by executing the method (700) are collectively utilized for analytic visualization of performance of braking algorithms and stopping distance calculation enabling the braking system to suggest the optimal time for application of brakes for safe locomotive halt.
[0052] FIG. 8 illustrates a flow chart (800) describing a procedure performed by a machine learning model, in accordance with an embodiment of the present disclosure.
[0053] In FIG. 8, a method (800) for training the machine learning (ML) model is described. The method (800) includes obtaining a journey data at step 802, pre-processing the journey data at step 804 by applying a variety of techniques such as, without limitations, imputing missing values and performing standard normalization on the journey data, extracting, at step 806, a set of features such as, without limitations, initial speed, final speed, time difference, type of brake, duration of brake and distance travelled, training a machine learning (ML) model at step 808 with the extracted features. In some embodiments, the ML model includes an ML ensemble model such as, without limitations, linear regression, support vector regression, random forest regression and saving the trained model at step 810 into a database, for example, the database may include the model storage component (408) of the data storage server (204) as shown in FIG. 4.
[0054] FIG. 9 illustrates an exemplary computer system (500) in which or with which embodiments of the present disclosure may be implemented.
[0055] As shown in FIG. 9, the computer system (900) may include an external storage device (910), a bus (920), a main memory (930), a read only memory (940), a mass storage device (950), communication port(s) (960), and a processor (970). A person skilled in the art will appreciate that the computer system (500) may include more than one processor (970) and communication port(s) (960). The processor (970) may include various modules associated with embodiments of the present disclosure. The communication port(s) (960) may be any of an RS-242 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port(s) (960) may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system connects. The memory (930) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (930) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or basic input/output system (BIOS) instructions for the processor (970). The mass storage device (950) may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), one or more optical discs, Redundant Array of Independent Disks (RAID) storage.
[0056] The bus (920) communicatively couples the processor (970) with the other memory, storage, and communication blocks. The bus (920) may be, e.g., a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB) or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (970) to the computer system (500).
[0057] Optionally, operator and administrative interfaces, e.g., a display, keyboard, and a cursor control device, may also be coupled to the bus (920) to support direct operator interaction with the computer system (900). Other operator and administrative interfaces may be provided through network connections connected through the communication port(s) (960). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (900) limit the scope of the present disclosure.
[0058] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE DISCLOSURE
[0059] The present disclosure performs advanced data analytics and computes various performance factors to evaluate and compare a performance efficiency of different braking algorithms to aid in better decision-making in terms of deployment of a specific algorithm in a locomotive for a safe halt.
[0060] The present disclosure computes the efficiency of different braking algorithms by filtering out braking data applied by the locomotive pilots thereby eradicating the effects caused by the driver intervention.
[0061] The present disclosure predicts a stopping distance and uses historical data related to brake application to estimate an optimal time for applying a brake in the locomotive to have a smooth halt.
, Claims:1. A brake evaluator system (110) for locomotives, said system (110) comprising:
a data wrangling server (202) to process raw data associated with a locomotive management system;
a data storage server (204) to store data from the data wrangling server (202);
a brake controller (206) to evaluate a performance of one or more braking methods deployed in the locomotives and predict a stopping distance to determine an optimal brake application time; and
a display unit (208) to display the determined optimal brake application time.

2. The brake evaluator system (110) as claimed in claim 1, wherein the brake controller (206) segregates a first brake data associated with a braking system from an another type of brake data applied by a locomotive pilot to evaluate the performance.

3. The brake evaluator system (110) as claimed in claim 1, wherein the raw data comprises at least one of: speed, brakes applied, absolute location, movement authority, data related to signaling types, signal direction, and current signal and next signal.

4. The brake evaluator system (110) as claimed in claim 1, wherein the data wrangling server (202) comprises a data collection component (302) for collecting the raw data, a data cleansing component (304) for detecting and correcting inaccuracies in the collected data, and a data transformation component (306) for transforming the data to be compatible with the brake evaluator system (110).

5. A method (600) for extracting brake data from journey data, said method (600) comprising:
obtaining (602) the journey data associated with each trip of a locomotive from a data storage server (204);
filtering (604) a locomotive data from the journey data;
computing (606) a speed gradient associated with the locomotive data for any two successive time intervals;
determining (608) a change in the speed gradient based on a threshold level; and
storing (610) the filtered data based on the change in the speed gradient being less than the threshold level.

6. The method (600) as claimed in claim 5, wherein filtering (604) the locomotive data is based on the locomotive data at a first-time instance when a brake is applied in the locomotive to the locomotive data at a second time instance until the impact of the brake is felt.

7. A method (700) for evaluating a brake performance by a brake controller (206), said method (700) comprising:
obtaining (702) a pre-processed data from a data storage server (204);
checking (704) a speed data associated with the pre-processed data;
assigning (706) a speed category to the pre-processed data based on the speed data and a speed threshold;
calculating (712, 714) one or more performance factors for the pre-processed data; and
storing (718) the calculated one or more performance factors in the data storage server (204).

8. The method (700) as claimed in claim 8, wherein the speed category comprises at least one of: low speed, medium speed, and high speed.
9. The method (700) as claimed in claim 7, comprising evaluating the brake performance based on the calculated one or more performance factors.

10. The method (700) as claimed in claim 7, wherein the one or more performance factors comprises a first performance factor based on an initial speed data, a final speed data, and a time difference data, and a second performance factor based on the initial speed data, the final speed data, the time difference data, and a duration of applying brakes.