Patent Publication Number: US-9415784-B2

Title: System and method for detecting wheel condition

Description:
TECHNICAL FIELD 
     The present disclosure relates generally to a detection system and method and, more particularly, a system and method for detecting a wheel condition. 
     BACKGROUND 
     Monitoring systems for the railroad industry provide methods and apparatuses for automatic determination of the temperatures of components including wheels and wheel bearings on passing trains. Infrared (IR) radiation radiating from the wheel or wheel bearing of a train traveling along a train track is indicative of a temperature or temperature range of the wheel or wheel bearing. IR scanners and associated circuits for detecting an overheated wheel or wheel hearing are available commercially. Some systems utilize an IR detector located in close proximity to the railroad tracks. The IR detector determines the presence of radiated IR waves within a predefined range of wavelengths. The IR detector also produces an output signal indicative of the power or intensity of the sensed IR radiation within the predefined range. 
     One problem associated with these types of systems for detecting a temperature range or a temperature of a railroad train wheel or wheel bearing involves inaccuracies that may result under different conditions. For example, in situations where the range of detected IR waves is attenuated or filtered by external sources such as blowing snow, wind, rain, or other weather conditions, the result is an inaccurate detection of a hot wheel or hot bearing condition. Accurate detection of an overheated component such as a wheel or wheel bearing allows for corrective actions to be taken before the overheated component breaks down or fails. 
     One attempt to avoid the problem of inaccurate detection of wheel and bearing temperatures in a harsh environment is disclosed in U.S. Pat. No. 6,872,945 to Bartonek that issued on Mar. 29, 2005 (the &#39;945 patent). The &#39;945 patent discloses an apparatus that includes a sensor for sensing IR radiation radiating from a train wheel or bearing within two or more IR wavelength ranges, where each wavelength range does not substantially overlap with any other wavelength range. The sensor generates signals indicative of the sensed IR radiation in each of the wavelength ranges. A processor determines a temperature range or a temperature of the wheel or wheel bearing from the generated signals. The temperature detection system of the &#39;945 patent may determine a temperature of a wheel or wheel bearing of a train traversing a railroad track. Furthermore, the temperature detection system of the &#39;945 patent may not be susceptible to variations in the amplitude, intensity, or power of the detected IR radiation. 
     Although the temperature detection system of the &#39;945 patent may be adequate for some applications, it may still be less than optimal. In particular, the temperature detection system of the &#39;945 patent may detect a temperature associated with projected locations of the wheels or wheel bearings of the train. While this process may be accurate in some applications, it does not account for the precise location and size of the wheel or wheel bearings and, thus, may provide less accurate temperature readings of these components. The temperature detection system of the &#39;945 patent may also not account for obstructions that restrict air flow to the wheels or wheel bearings of the train. The restriction of air flow can cause the wheels or wheel bearings to get warmer than normal, which can lead to false hot bearing detections that slow operations and reduce efficiencies of the train. Additionally, the temperature detection system of the &#39;945 patent generally only considers the temperature of the wheels and/or wheel bearings of the train, without considering other wheel and wheel bearing conditions, such as, for example, movement of the wheels relative to the train track. 
     The system and method of the present disclosure solves one or more problems set forth above and/or other problems in the art. 
     SUMMARY 
     In one aspect, the present disclosure is directed to a method for detecting a condition associated with a wheel on a train car. The method may include detecting a position of the wheel. The method may also include detecting a position of a rail on which the wheel travels. The method may further include comparing the position of the wheel relative to the position of the rail, and determining a wheel condition based on the comparison. 
     In another aspect, the present disclosure is directed to a system for detecting a condition associated with a wheel on a train car. The system may include at least one sensor configured to generate at least one thermal image, and a processor. The processor may be configured to detect a position of the wheel based on the at least one thermal image. The processor may also be configured to detect a position of a rail on which the wheel travels based on the at least one thermal image. The processor may further be configured to compare the position of the wheel relative to the position of the rail, and determine a wheel condition based on the comparison. 
     In yet another aspect, the present disclosure is directed to a method for detecting a condition associated with a wheel on a train car. The method may include receiving at least one thermal image from at least one sensor. The method may also include detecting a position of a flange of the wheel based on the at least one thermal image, and detecting a position of a rail on which the wheel travels based on the at least one thermal image. The method may further include comparing the position of the flange relative to the position of the rail over a predetermined period of time, and determining a wheel condition based on the comparison. The method may further include implementing a control action based on the determined wheel condition. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagrammatic illustration of an exemplary disclosed train; 
         FIG. 2  is a diagrammatic illustration of an exemplary undercarriage portion of the train of  FIG. 1 ; 
         FIG. 3  is a schematic illustration of an exemplary disclosed detection system that may be used with the train of  FIG. 1 ; 
         FIG. 4  is a flowchart depicting an exemplary disclosed method that may be performed by the system of  FIG. 2 ; and 
         FIG. 5  is a flowchart depicting another exemplary disclosed method that may be performed by the system of  FIG. 2 . 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  illustrates a portion of a train  100  including one or more cars  110 ,  120 . Each car, such as shown for car  110 , may include a plurality of trucks, such as trucks  122  and  124 . A car may have as many as ten or more trucks, although more typically the number of trucks is two per car. Each truck  122 ,  124  may include two or more axles, with wheel bearings  150 ,  152  shown at one end of each of the axles on truck  122 , and wheel bearings  154 ,  156  shown at one end of each of the axles on truck  124 . 
     In addition, train  100  may include a pneumatic braking system, which may include a main air line from the locomotive (not shown), from which pressurized air is supplied to various brake valves, such as brake valve  160  shown in  FIG. 1 . Brake valve  160  may control the operation of one or more brake cylinders (not shown), which may control the actuation of one or more brakes  140 ,  142 ,  144 ,  146 . Each brake  140 ,  142 ,  144 ,  146  may include friction material configured for contact against respective wheels  130 ,  132 ,  134 ,  136 . One of ordinary skill in the art will recognize that other alternative brake systems may include disc brake systems or hydraulic fluid braking systems. 
     In various implementations, wheels  130 ,  132 ,  134 ,  136  may be paired with matching wheels at the opposite ends of their respective axles. For example, as shown in  FIG. 2 , wheel  130  may be connected to a matching wheel  130  via an associated axle  133 . Similarly, wheel bearing  150  may be paired with a matching wheel bearing  150  at the opposite end of axle  133 . It is contemplated that, although only outer wheel bearings are shown in  FIG. 2 , one of ordinary skill in the art would recognize that inner wheel bearings may also be utilized, as desired. 
     As shown in  FIG. 2 , wheels  130  may include flanges  131  that are configured to guide wheels  130  along rails  170  of a train track. Although not shown in  FIG. 2 , matching wheels, matching wheel bearings, and flanges associated with each of the wheels may be associated with the other wheels  132 ,  134 ,  136  and wheel bearings  152 ,  154 ,  156  of train  100 . Additionally, in some embodiments, train car  110  may include one or more portions  111  that extend downward to cover at least part of wheel bearings  150 , such that the Wheel hearings  150  may not be visible while standing at a location exterior to train  100 . 
     During operation of the train, various components may wear out from continued use, and worn components may result in generation of excessive heat, which in turn may lead to failure of the components or potentially unsafe conditions. Therefore, various implementations of the present disclosure may monitor wheel temperatures and wheel bearing temperatures, as will be discussed in more detail below. In addition, various implementations of the present disclosure may also monitor one or more wheel conditions (e.g., movements of the wheels relative to the rails) and/or one or more wheel bearing conditions to prevent other potentially unsafe conditions (e.g., derailment of the train from the rails), gas will be discussed in more detail below. 
       FIG. 3  illustrates an exemplary implementation of the disclosure directed to a detection system  200  for detecting one or more conditions of wheels  130 ,  1 . 32 ,  134 ,  136 , and one or more conditions of wheel bearings  150 ,  152 ,  154 ,  156  on train cars  110 ,  120  (shown in  FIG. 1 ) moving along rails  170  of the train track. Wayside detectors  211 ,  212 ,  213  may be positioned along rails  170  to automatically sense conditions of wheels  130 ,  132 ,  134 ,  136  or conditions of wheel bearings  150 ,  152 ,  154 ,  156  of a passing train. Signals output from detectors  211 ,  212 ,  213  may be processed to enable an alarm when the wheel or wheel bearing conditions are no longer safe for continued operation. Alternatively or additionally, the signals output from detectors  211 ,  212 ,  213  may assist in determining when to schedule preventative maintenance for avoidance of possible failures or unsafe conditions. For example, the alarm may be enabled when the wheel or wheel bearing temperatures become too great for continued safe operation, or when the sensed temperatures may be exhibiting a magnitude or trend in magnitudes that may assist in determining when to schedule preventative maintenance. The alarm may also be enabled when movements of one or more wheels  130 ,  132 ,  134 ,  136  relative to rails  170  are detected to be unsafe for continued operation, or are exhibiting a magnitude or trend in magnitudes that may assist in determining when to schedule preventative maintenance. 
     Detectors  211 ,  212 ,  213  in  FIG. 3  may be positioned and configured to detect conditions of the wheels, wheel bearings, or other components of a passing train. Detectors  211 ,  212 , and  213  may include, temperature sensors  222 - 227 , which are configured to convert sensed infrared (IR) radiation energy produced by a component such as a passing train wheel or wheel bearing to an electrical signal that is proportional to the amount of heat output by the wheel or wheel bearing relative to ambient temperature. Temperature sensors  222 - 227  may also detect photons emitted from an object being measured. Temperature sensors  222 - 227  may include, but are not limited to, IR non-imaging sensors, IR imaging sensors, photovoltaic sensors, piezoelectric sensors, pyroelectric sensors, and thermopile sensors. In one embodiment, temperature sensors  222 - 227  may include an IR imaging sensor, which may collect IR radiation at multiple points, which can form an array, and can provide data that may be used to create one or more thermal images. 
     Detectors  211 ,  212 ,  213  and temperature sensors  222 - 227  may be located in different positions relative to rails  170 . Detectors  211 ,  212 ,  213 , and temperature sensors  222 - 227  may be located in positions adjacent rails  170  and in between rails  170 . They may also be located in housings configured to replace select ties provided to support rails  170  (sometimes referred to as “sleeper ties”). Other locations for temperature detectors  211 ,  212 ,  213 , and temperature sensors  222 - 227 , may include positions adjacent rails  170  and to the outside of rails  170 , at angles looking up from ground level, at angles looking in a horizontal direction from an elevated position adjacent rails  170  and at approximately the height of wheel bearings  150 ,  152 ,  154 ,  156 , and at angles looking down toward ground level from an elevated position adjacent rails  170 . One of ordinary skill in the art will recognize that there are a variety of temperature sensing technologies suitable for use with various implementations of the disclosure. 
     As shown in  FIG. 3 , at least a first temperature sensor  222 , and a second temperature sensor  223 , may be disposed on opposite sides of rails  170  in order to be able to detect the temperatures of wheels or wheel bearings on both sides of a passing train car. Temperature sensors  224  and  225  associated with detector  212 , may be positioned on opposite sides of rails  170  at the same location or at predetermined spacing, and at a predetermined spaced interval along rails  170  from detector  211 . Optional additional pairs of temperature sensors associated with additional detectors, such as temperature sensors  226  and  227  associated with detector  213 , may also be positioned on opposite sides of rails  170  at the same location or at predetermined spacing, and disposed at predetermined spaced intervals further along rails  170  in a direction of train travel along rails  170 . In various exemplary implementations, the distance between each detector  211 ,  212 ,  213  may be any distance from several hundred yards, to approximately one half mile, to one mile or more. 
     The spaced pairs of temperature sensors may be included in a pre-designated detection area  210  along rails  170 . Multiple detection areas similar to detection area  210  may be spaced along rails  170 , with each detection area including two or more spaced pairs of temperature sensors. The detection areas may be located along stretches of train track over varying terrains. The pairs of temperature sensors  222  and  223 ,  224  and  225 , and  226  and  227  placed along opposite sides of rails  170  may produce signals indicative of the temperatures for each wheel or wheel bearing on a per axle basis, and may provide those signals to associated detectors  211 ,  212 ,  213 , respectively. Each detector  211 ,  212 ,  213  may also include associated position sensors  232  and  233 ,  234  and  235 , and  236  and  237 , respectively. 
     As a train car wheel passes each detector  211 ,  212 ,  213 , the associated pairs of position sensors position sensors  232 - 237  may provide signals to the associated detectors  211 ,  212 ,  213 . Each associated detector  211 ,  212 ,  213  may use the signals from position sensors  232 - 237  in defining a window when signals from associated temperature sensors  222 - 227  may be received and converted into temperatures of passing wheels  130 ,  132 ,  134 ,  136  or wheel bearings  150 ,  152 ,  154 ,  156 . Each of detectors  211 ,  212 , and  213  may be positioned at wayside stations along rails  170 , and may be communicatively coupled with a processor  215  of detection system  200 . Processor  215  may be located remotely from rail  170 , in a dispatch office, on board the train, or in one or more wayside stations. Signals  252 ,  254 ,  256  may be communicated from detectors  211 ,  212 ,  213 , respectively, to processor  215 . One of ordinary skill in the art will recognize that signals  252 ,  254 , and  256  may be communicated to processor  215  through a wireless connection, over an ethernet connection, over a network, or through other means. Alternatively, each detector  211 ,  212 ,  213  may include an autonomous processor configured to perform various functions on the data received from temperature sensors  222 - 227  and from position sensors  232 - 237 . It is contemplated that, in some embodiments, position sensors  232 - 237  may be omitted, and processor  215  and temperature sensors  222 - 227  may instead perform its functions. 
     Processor  215  may also be configured to only activate temperature sensors  222 - 227  of detectors  211 ,  212 , and  213  when associated position sensors  232 - 237  indicate the presence of a wheel within the window between each pair of wheel position sensors. One of ordinary skill in the art will recognize that various implementations may include position sensors  232 - 237  comprising physical proximity transducers positioned adjacent train track  170 , as shown in  FIG. 3 . In alternative implementations, the position of a wheel on a train car may be determined by other wheel position locators including devices that analyze a global positioning system (GPS) signal associated with a position of the train car. These wheel position locators may be located on the train  100 , alongside the track  170 , in wayside station houses, or in other remote locations. A GPS receiver  105  (shown in  FIG. 1 ) may be located on a train car  110 ,  120  to provide wheel position location capabilities, identify where the train car is within detection area  210  at any time relative to the detectors, etc. 
     One of ordinary skill in the art will recognize that, although processor  215  is illustrated as a single unit, the functionality provided by processor  215  could be provided instead by one or more processors. The one or more processors may be part of a server, client, network infrastructure, mobile computing platform, or a stationary computing platform, one or more of which may be contained in a dispatch office, on the train, in a single wayside housing, multiple wayside housings, or at remote locations communicatively coupled over wired or wireless networks. Additionally, it is contemplated that temperature sensors  222 - 227  and position sensors  232 - 237  may have one or more associated processors to perform, for example, digitalization of signals and/or filtering the signals. 
     Detectors  211 ,  212 ,  213  capable of providing data indicative of the temperature of wheels  130 ,  132 ,  134 ,  136  and wheel bearings  150 ,  152 ,  154 ,  156  on a passing train  100  may include infrared (IR) sensors that react to IR radiation emitted by wheels  130 ,  132 ,  134 ,  136  and wheel hearings  150 ,  152 ,  154 ,  156  during operation of train  100  as a result of friction, transfers of vibrational energy, or other conditions that result in the generation of thermal energy. The IR detectors may receive IR radiation emitted from wheels  130 ,  132 ,  134 ,  136  or wheel bearings  150 ,  152 ,  154 ,  156  as a train passes the location of the IR detectors, and the IR radiation may be focused through one or more lenses, reflected by one or more reflective optics, or otherwise processed before reaching the IR detectors. 
     Various implementations of the present disclosure may provide data indicative of one or more wheel conditions and/or wheel bearing conditions of wheels  130 ,  132 ,  134 ,  136  and wheel bearings  150 ,  152 ,  154 ,  156  on a passing train  100  based on signals received from temperature sensors  222 - 227  and from position sensors  232 - 237 . For example, processor  215  may be configured to receive signals  252 ,  254 , and  256  from detectors  211 ,  212 ,  213 , which may include one or more thermal images based on the signals received from temperature sensors  222 - 227  and from position sensors  232 - 237 . Using the thermal images, processor  215  may be configured to determine positions of wheels  130 ,  132 ,  134 ,  136 , wheel bearings  150 ,  152 ,  154 ,  156 , and/or rail  170 . Processor  215  may also be configured to use one or more predetermined spatial filtering algorithms to identify the size and locations of each of these components. In addition, processor  215  may be configured to detect a presence of any obstructions causing restriction in air flow to wheels  130 ,  132 ,  134 ,  136  and wheel bearings  150 ,  152 ,  154 ,  156 . Based on these detections, processor  215  may determine various wheel conditions and/or wheel bearing conditions, and determine whether to implement a control action based on the detected wheel conditions and/or wheel bearing conditions. 
       FIGS. 4 and 5  are flowcharts depicting exemplary disclosed methods that may be performed by the system of  FIG. 3 .  FIGS. 4 and 5  will be discussed in more detail below to further illustrate the disclosed concepts. 
     INDUSTRIAL APPLICABILITY 
     The disclosed method and system may allow for more accurate detection of wheel and wheel bearing temperatures as well as detecting potentially unsafe conditions associated with the wheels and the wheel bearings on a train car. Specifically, the disclosed method and system may detect the exact size and location of the wheels and the wheel bearings of the train car to more accurately determine temperatures associated with these components. In addition, the disclosed method and system may determine whether the movements of a wheel relative to rails on which the wheel travels are safe for continued operation. The disclosed method and system may further detect the presence of obstructions that restrict air flow to the wheels or wheel bearings. The detection of one or more of these wheel conditions and wheel bearing conditions may be used to identify when certain control actions should be taken, such as stopping the train to perform maintenance, scheduling future maintenance, performing autonomous control, etc. 
     Aspects of the present disclosure provide the functionality of detecting potentially unsafe operating conditions by tracking critical components of the train car through the use of the temperature and position sensors. The temperature sensors may output one or more thermal images, which may indicate excessive heating of wheels or wheel bearings of the train car. Aspects of the present disclosure may use spatial filtering algorithms to identify positions, sizes, and locations of the wheels and wheel bearings, in order to more accurately determine temperatures associated with these components. In addition, the spatial faltering algorithms may also allow the system to identify any obstructions restricting air flow to the wheels and wheel bearings. The restricted air flow may cause the temperatures of the wheels and wheel bearings to be higher than normal. By identifying the presence of these obstructions, false hot wheel or hot wheel bearing detections can be prevented with the use of spatial filtering algorithms. Aspects of the present disclosure may also detect various movements of the wheels relative to the rails in which they travel on. In certain situations, a flange of the wheel may be grinding against the rails at an angle or straight against the rails. This grinding may cause excessive wearing of the wheels and/or the rails, which can ultimately lead to unsafe conditions if not properly diagnosed or corrected. Also, if the flange is oscillating rapidly in a direction traverse to the rails (commonly known as “wheel hunting”), this may cause derailment of the train if the wheels were to become misaligned with the rails. Therefore, by detecting such wheel or wheel bearing conditions, derailment and/or excessive wearing of the wheels, wheel bearings, or rails can be prevented. 
     When one or more of these wheel conditions or wheel bearing conditions are detected, a processor  215  may be configured to implement a control action. The control actions implemented by the processor  215  may include, but are not limited to, sending an alert, sounding an alarm, sending instructions to be followed manually by a train operator, performing autonomous control of the train, scheduling maintenance functions to be performed at a later time, etc. Various implementations of this disclosure provide a detection system that may allow for the automated detection of wheel and/or wheel hearing conditions on every train car on a train as the train is moving along a train track. 
     Referring to  FIG. 4 , in one exemplary implementation, at step  302 , processor  215  may obtain sensor data via signals  252 ,  254 , and  256  from detectors  211 ,  212 ,  213  and associated temperature sensors  222 - 227  and position sensors  232 - 237 . Then, at step  304 , processor  215  may use the sensor data to detect a position of a wheel  130  of train  100  within a designated detection area.  210  as the train  100  passes through the detection area  210 . For example, signals output from temperature sensors  222 - 227  may provide one or more thermal images of train  100 . Processor  215  may then detect a position of wheel  130  based on the thermal images using one or more spatial filtering algorithms. In some embodiments, processor  215  may also detect a position of flange  131  associated with wheel  130  using the thermal images and spatial filtering algorithms. 
     At step  306 , processor  215  may detect a position of a rail  170  within detection area  210  as train  100  passes through the detection area  210 . Similar to wheel  130 , processor  215  may also use the thermal images and the spatial filtering algorithms to detect the position of the rail  170 . Rail  170  may be the rail on which wheel  130  is travelling at the moment train  100  passes through the detection area  210 . 
     At step  308 , processor  215  may compare the detected positions of wheel  130  and rail  170 . For example, processor  215  may determine a distance between wheel  130  and rail  170 . In some embodiments, the distance may be a distance between flange  131  and rail  170 . Alternatively or additionally, processor  215  may compare the detected positions of wheel  130  and rail  170  over a predetermined period of time. For example, the detected positions of wheel  130  and rail  170  may be detected at a first location at detector  211  and then again at a second location at detector  212  after the predetermined period of time. Based on the detected positions of wheel  130  and rail  170  and the time that elapsed while train  100  traveled between detectors  211  and  212 , processor  215  may determine a velocity and/or an acceleration of wheel  130  relative to rail  170 . The velocity and/or acceleration of wheel  130  may indicate, for example, potentially dangerous oscillation of wheel  130  in a direction traverse to rails  170 . 
     At step  310 , data gathered and processed by processor  215  may be used to identify when certain control actions should be taken, such as stopping the train to perform maintenance, scheduling future maintenance, and performing autonomous control. In some embodiments, a threshold value may be associated with a number of factors used in determining whether the control actions should be taken. For example, the threshold value may include information based on the distance between wheel  130  and rail  170 , a velocity of wheel  130  relative to rail  170 , and/or an acceleration of wheel  130  relative to rail  170 . The threshold value may be exceeded when the distance between wheel  130  and rail  170  is less than a predetermined distance, when the velocity of wheel  130  relative to rail  170  is higher than a predetermined velocity, and/or when the acceleration of wheel  130  relative to rail  170  is higher than a predetermined acceleration. At step  310 , if the threshold value is exceeded, then processor  215  may generate one or more control actions (e.g., activate an alarm) at step  312 . Otherwise, the process may return to step  302  to receive more sensor data. 
     Referring to  FIG. 5 , in another exemplary implementation, at step  402 , processor  215  may obtain sensor data via signals  252 ,  254 , and  256  from detectors  211 ,  212 ,  213  and associated temperature sensors  222 - 227  and position sensors  232 - 237 . Then, at step  404 , processor  215  may use the sensor data to detect a size and a location of a wheel bearing  150  within a designated detection area  210  as train  100  passes through the detection area  210 . For example, signals output from temperature sensors  222 - 227  may provide one or more thermal images. Processor  215  may then detect the size and the location of a wheel bearing  150  based on the thermal images using one or more spatial filtering algorithms. In addition to detecting the size and the location of wheel bearing  150 , it is contemplated that processor  215  may also detect the size and the location of a wheel  130  based on the thermal images using one or more spatial filtering algorithms. 
     At step  406 , processor  215  may use the detected size and location of wheel bearing  150  to detect a temperature associated with wheel bearing  150 . Specifically, using the thermal images and the spatial filtering algorithms, processor  215  may determine an exact size and location of wheel bearing  150  in the thermal images and extract thermal imaging data based on the size and location of wheel bearing  150 . In some embodiments, processor  215  may extract thermal imaging data across an entire bearing cup (not shown) associated with wheel bearing  150 . The bearing cup may cover, for example, one or more bearing assemblies of wheel bearing  150 . As a result, this process may provide a more accurate temperature reading than other systems, which tend to predict a size and location of the wheel bearings rather than determine the size and location based on thermal images. In addition to a temperature associated with wheel bearing  150 , it is contemplated that processor  215  may also detect a temperature associated with wheel  130  using the thermal images and the spatial filtering algorithms. 
     At step  408 , processor  215  may determine whether the temperature of wheel bearing  150  exceeds a threshold value. For example, if the temperature of wheel bearing  150  is greater than a threshold temperature, then the process may proceed to step  410 . Otherwise, the process may return to step  402  to receive more sensor data. 
     At step  410 , processor  215  may determine whether an obstruction has caused the wheel bearing temperature to become abnormally warm. For example, one such obstruction may be a portion  111  (shown in  FIG. 2 ) of train car  110  that covers at least part of wheel bearings  150 , thereby restricting air flow to wheel bearings  150 . Although the restricted air flow may cause the wheel bearing temperature to become abnormally warm, this does not necessarily mean that wheel bearing  150  is subject to a hot bearing condition. Therefore, if an obstruction is not located a predetermined distance from wheel bearing  150 , then processor  215  may generate one or more control actions (e.g., activate an alarm) at step  412 . Otherwise, the process may return to step  402  to receive more sensor data. 
     It will be apparent to those skilled in the art that various modifications and variations can be made to the disclosed detection system without departing from the scope of the disclosure. Other embodiments of the detection system will be apparent to those skilled in the art from consideration of the specification and practice of the detection system disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope of the disclosure being indicated by the following claims and their equivalents.