Structural health monitoring devices and related systems and methods

Abstract

The invention relates to structural health monitoring devices and related systems and methods. A device for monitoring a structural health of a subject includes a plurality of sensor pairs. The plurality of sensor pairs each include an excitation sensor and a reception sensor. The excitation sensor is configured to transmit a guided wave, and the reception sensor is configured to receive the guided wave. The plurality of sensor pairs are used to acquire individual features from each to generate a plurality of individual features. A pseudo-baseline feature is generated by calculating an average value of the plurality of individual features and compared to individual features of a respective one of the plurality of sensor pairs to determine whether the individual feature is different from the pseudo-baseline feature. If the individual features are different, a neighbor region of the object of a respective one of the plurality of sensor pairs is identified as a potential anomalous region.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to monitoring the structural health of a subject, and more particularly to utilizing a pseudo baseline signature to monitor the structural health of a subject. BACKGROUND

[0002] Structural health monitoring evaluates the condition of a subject to detect any anomalies or defects within its structure. One monitoring method utilizes guided waves, which are mechanical waves that travel along a surface or through a subject structure. To detect anomalies, a current wave signature is compared to a baseline signature, a reference measurement obtained prior to any known problems.

[0003] However, acquiring baseline signatures presents several challenges, such as each baseline signature being specific to a particular subject and having to be measured and stored separately. Further, baseline signatures are very dependent on environmental conditions, requiring compensation algorithms to account for changes in environmental conditions between baseline signatures and current wave signatures. Further still, measuring baseline signatures on a subject in situ is difficult because anomalies are detected relative to an initial state that is unknown or potentially defective at the time of baseline signature capture. Thus, current methods for acquiring and utilizing baseline signatures for structural health monitoring are time consuming, inefficient, and complex. SUMMARY

[0004] The development of the present application subject matter is in response to problems and desires yet to be fully solved by the current state of the art, particularly in response to problems and desires arising from existing structural health monitoring devices and related systems and methods. Generally, the present application subject matter has been developed to provide a structural health monitoring device and related systems and methods that overcome at least some of the aforementioned shortcomings of the prior art.

[0005] Disclosed herein are apparatuses for monitoring the structural health of an object, the apparatuses including a plurality of sensor pairs, a processor, and a memory storing code executable by the processor. The plurality of sensor pairs are configured to be coupled to the object. Each of the plurality of sensor pairs includes an excitation sensor and a receiving sensor. The excitation sensor is configured to transmit a guided wave, and the receiving sensor is configured to receive the guided wave transmitted by the excitation sensor. The plurality of sensor pairs are geometrically similar to one another. The code is executable by the processor to obtain an individual signature from each of the plurality of sensor pairs, thereby generating a plurality of individual signatures. Each individual signature is representative of the guided wave received by the receiving sensor in a respective one of the plurality of sensor pairs. The code is also executable by the processor to generate a pseudo-baseline signature by calculating a mean of the plurality of individual signatures. The code is further executable by the processor to compare the individual signature of a respective one of the plurality of sensor pairs to the pseudo-baseline signature to determine whether the individual signature of the respective one of the plurality of sensor pairs is different from the pseudo-baseline signature. If the individual signature of the respective one of the plurality of sensor pairs is different, the code is additionally executable to identify a region in the object proximate to the respective one of the plurality of sensor pairs as a potential anomalous region. The foregoing subject matter of this paragraph describes Example 1 of the disclosure.

[0006] The guided wave transmitted by the excitation sensor in each of the plurality of sensor pairs is a lamb wave. The foregoing subject matter of this paragraph describes Example 2 of the disclosure, wherein Example 2 further includes the subject matter described above according to Example 1.

[0007] The guided wave transmitted by the excitation sensor in each of the plurality of sensor pairs is a surface wave. The foregoing subject matter of this paragraph describes Example 3 of the disclosure, wherein Example 3 further includes the subject matter described above according to Example 1.

[0008] The comparison of the pseudo-baseline signature to the individual signature of a respective one of the plurality of sensor pairs is performed immediately after generating the pseudo-baseline signature. The foregoing subject matter of this paragraph describes Example 4 of the disclosure, wherein Example 4 further includes the subject matter described above according to any of Examples 1-3.

[0009] The geometric similarity of each of the plurality of sensor pairs includes each of the plurality of sensor pairs having a same distance between the excitation sensor and the receiving sensor, and each of the plurality of sensor pairs having a consistent spatial displacement between the excitation sensor and the receiving sensor in a three-dimensional space. The foregoing subject matter of this paragraph describes Example 5 of the disclosure, wherein Example 5 further includes the subject matter described above according to any of Examples 1-4.

[0010] The memory stores code that is executable by the processor to define an anomaly index based on a degree of deviation between the pseudo-baseline feature and the individual features of the plurality of pairs of sensors. The memory also stores code that is executable by the processor to define an individual index based on a degree of deviation between the pseudo-baseline feature and the individual features of a respective one of the plurality of pairs of sensors. The anomaly index and the individual index are numerical values. If the individual index is greater than the anomaly index, then the respective one of the plurality of pairs of sensors is different from the pseudo-baseline feature, such that a region of the subject proximate to the respective one of the plurality of pairs of sensors is identified as a potentially anomalous region. The foregoing subject matter of this paragraph describes Example 6 of the disclosure, where Example 6 further includes the subject matter described above according to any of Examples 1-5.

[0011] If the individual features of a respective one of the plurality of pairs of sensors are similar to the pseudo-baseline feature, then a region of the subject proximate to the respective one of the plurality of pairs of sensors is identified as a structurally normal region. The foregoing subject matter of this paragraph describes Example 7 of the disclosure, where Example 7 further includes the subject matter described above according to any of Examples 1-6.

[0012] The plurality of pairs of sensors are configured to operate in a continuous monitoring mode. The processor continuously acquires updated individual features from each of the plurality of pairs of sensors, generates an updated pseudo-baseline feature, and compares the updated individual features of each of the plurality of pairs of sensors to the updated pseudo-baseline feature to provide real-time detection and identification of potentially anomalous regions. The foregoing subject matter of this paragraph describes Example 8 of the disclosure, where Example 8 further includes the subject matter described above according to any of Examples 1-7.

[0013] Further disclosed herein is a structural health monitoring system that includes an object whose structural health is to be monitored and a plurality of sensor pairs coupled to the object. Each of the plurality of sensor pairs includes an excitation sensor and a receiving sensor. The excitation sensor is configured to transmit a guided wave and the receiving sensor is configured to receive the guided wave transmitted by the excitation sensor. The plurality of sensor pairs are geometrically similar to each other and the object structure through which the guided wave propagates for each of the plurality of sensor pairs is the same. The structural health monitoring system further includes a processor and a memory that stores code executable by the processor. The code is executable to obtain an individual signature from each of the plurality of sensor pairs to generate a plurality of individual signatures. Each individual signature is representative of the guided wave received by the receiving sensor of a respective one of the plurality of sensor pairs. The code is further executable to generate a pseudo-baseline signature by calculating a mean of the plurality of individual signatures and compare the individual signature of a respective one of the plurality of sensor pairs to the pseudo-baseline signature to determine whether the individual signature of the respective one of the plurality of sensor pairs is different from the pseudo-baseline signature. If the individual signature of the respective one of the plurality of sensor pairs is different, the code is further executable to identify a region of the object proximate to the respective one of the plurality of sensor pairs as a potential anomalous region. The foregoing subject matter of this paragraph describes Example 9 of the disclosure.

[0014] The guided wave transmitted by the excitation sensor in each of the plurality of sensor pairs is configured to propagate through a thickness of the object in a propagation direction that is parallel to a surface of the object. The foregoing subject matter of this paragraph describes Example 10 of the disclosure, wherein Example 10 further includes the subject matter described above according to Example 9.

[0015] The guided wave transmitted by the excitation sensor in each of the plurality of sensor pairs is configured to propagate along a surface of the object in a circular or elliptical motion from the excitation sensor. The foregoing subject matter of this paragraph describes Example 11 of the disclosure, wherein Example 11 further includes the subject matter described above according to Example 9.

[0016] The plurality of sensor pairs are located on a surface of the structure of the object. The foregoing subject matter of this paragraph describes Example 12 of the disclosure, wherein Example 12 further includes the subject matter described above according to any one of Examples 9-11.

[0017] The plurality of sensor pairs are embedded within the structure of the object. The foregoing subject matter of this paragraph describes Example 13 of the disclosure, wherein Example 13 further includes the subject matter described above according to any one of Examples 9-12.

[0018] The plurality of sensor pairs are coupled to or positioned proximate to a plurality of rivet pairs on the object, individual rivets in each of the plurality of rivet pairs being equidistantly spaced from one another. The foregoing subject matter of this paragraph describes Example 14 of the present disclosure, wherein Example 14 further comprises the subject matter described above according to any of Examples 9-13.

[0019] The object to be monitored for anomalies is an aircraft. The foregoing subject matter of this paragraph describes Example 15 of the present disclosure, wherein Example 15 further comprises the subject matter described above according to any of Examples 9-14.

[0020] The plurality of sensor pairs are coupled to a local region of the object. The local region of the object corresponds to a region of the object that is susceptible to anomalies. The foregoing subject matter of this paragraph describes Example 16 of the present disclosure, wherein Example 16 further comprises the subject matter described above according to any of Examples 9-15.

[0021] Further disclosed herein is a method of monitoring structural health of an object. The method includes obtaining an individual signature from each of a plurality of sensor pairs to generate a plurality of individual signatures. Each individual signature represents a guided wave transmitted by an excitation sensor and received by a receiving sensor of a respective one of the plurality of sensor pairs. The method further includes generating a pseudo-baseline signature by calculating an average of the plurality of individual signatures. The method further includes comparing the individual signature of the respective one of the plurality of sensor pairs to the pseudo-baseline signature to determine whether the individual signature of the respective one of the plurality of sensor pairs is different from the pseudo-baseline signature. If the individual signature of the respective one of the plurality of sensor pairs is different, the method further includes identifying a region of the object proximate to the respective one of the plurality of sensor pairs as a potential anomalous region. The foregoing subject matter of this paragraph describes Example 17 of the present disclosure.

[0022] The method includes defining an anomaly index based on a degree of deviation between the pseudo-baseline signature and the plurality of individual signatures. The method further includes defining an individual index based on a degree of deviation between the pseudo-baseline signature and the individual signature of the respective one of the plurality of sensor pairs. If the individual index is greater than the anomaly index, the respective one of the plurality of sensor pairs is different from the pseudo-baseline signature, such that the region of the object proximate to the respective one of the plurality of sensor pairs is identified as a potential anomalous region. The foregoing subject matter of this paragraph describes Example 18 of the present disclosure, wherein Example 18 further comprises the subject matter described above according to Example 17.

[0023] The plurality of sensor pairs are configured to operate in a periodic monitoring mode such that the individual signature from each of the plurality of sensor pairs is obtained and compared to the pseudo-baseline signature at specific monitoring times to detect and identify potential anomalous regions. The foregoing subject matter of this paragraph describes Example 19 of the present disclosure, wherein Example 19 further comprises the subject matter described above according to any of Examples 17-18.

[0024] The plurality of sensor pairs are configured to operate in a continuous monitoring mode, continuously acquiring updated individual features from each of the plurality of sensor pairs, generating updated pseudo-baseline features, and comparing the updated individual features from each of the plurality of sensor pairs with the updated pseudo features, thereby enabling real-time detection and identification of potential abnormal regions. The foregoing subject matter of this paragraph describes example 20 of the present disclosure, wherein example 20 further comprises the subject matter described above according to any of examples 17-19.

[0025] The features, structures, advantages, and / or characteristics of the subject matter of the present disclosure can be combined in any suitable manner in one or more instances. In the following description, numerous specific details are provided to give a thorough understanding of examples of the subject matter of the present disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, details, components, materials, and / or methods, in some instances. Other components, materials, and / or methods can be BRIEF DESCRIPTION OF DRAWINGS

[0026] For the subject matter to be more readily understood, a more particular description of the subject matter briefly described above will be rendered by reference to specific examples that are illustrated in the drawings. It is to be understood that the description and specific examples are merely intended to be illustrative and that the subject matter is not to be limited thereto. The subject matter will be more fully understood and appreciated by reference to the following description and drawings.

[0027] Figure 1 is a schematic diagram of a device for monitoring structural health of a subject in accordance with one or more examples of the present disclosure;

[0028] Figure 2A is a schematic graphical view of a plurality of individual features corresponding to a plurality of sensors in accordance with one or more examples of the present disclosure;

[0029] Figure 2B is a schematic graphical view of pseudo-baseline features of the plurality of sensor pairs in accordance with one or more examples of the present disclosure;

[0030] Figure 2C is a schematic graphical view of pseudo-baseline features of the plurality of sensor pairs in accordance with one or more examples of the present disclosure; Figure 2B Figure 2A ​schematic graphical view of individual feature comparisons;

[0031] Figure 3A schematic cross-sectional view of an object in accordance with one or more examples of the present disclosure, with sensor pairs on a surface of the object, with an excitation sensor sending a guided wave to a receiving sensor;

[0032] Figure 3B schematic perspective view of an object in accordance with one or more examples of the present disclosure Figure 3A schematic cross-sectional view of an object with an anomaly within the object

[0033] Figure 4 schematic perspective view of an object in accordance with one or more examples of the present disclosure, with a plurality of rivet pairs, with a pair of the rivet pairs having a respective sensor pair; and

[0034] Figure 5 schematic flowchart of a method of monitoring structural health of an object in accordance with one or more examples of the present disclosure. DETAILED DESCRIPTION

[0035] References throughout this specification to "one example", "an example", or similar language mean that a particular feature, structure, or characteristic described in connection with the example is included in at least one example of the subject matter disclosed. The appearance of the phrase "in one example" or "in an example" or similar language in various places in the specification is not necessarily referring to the same example. Similarly, the use of terminology "implementation" or "implementation(s)" means an implementation having the described features, structures or characteristics of one or more examples of the subject matter disclosed, however, in the absence of explicit indication to the contrary, the implementation can be related to one or more examples.

[0036] Examples of devices and related systems and methods for monitoring the structural health of a subject are disclosed herein. Some features of at least some examples of the devices and related systems and methods are provided below. The structural health devices are used to monitor the structural health of a subject without requiring baseline characteristics. As used herein, baseline characteristics refer to reference measurements of the subject being monitored taken prior to the presence of anomalies, i.e., detect-free. Baseline characteristics serve as a comparison standard for subsequent characteristics to detect any changes or anomalies. However, obtaining baseline characteristics of the subject being monitored is not always possible or practical due to various issues with acquiring detect-free baseline characteristics. For example, if the subject is already in use, i.e., in-service, any baseline characteristics obtained will inherently include any pre-existing anomalies that were present at the time of capture. In other words, if the baseline characteristics, which are meant to represent a detect-free state of the subject, contain anomalies, they cannot serve as an accurate reference for detecting any anomalies in the structure, thereby compromising the reliability of the structural health monitoring process. Furthermore, any baseline characteristic comparison must account for and apply compensation algorithms for environmental differences, such as weather, between the baseline characteristics and subsequent characteristics, adding complexity to obtaining and utilizing baseline characteristics.

[0037] Accordingly, the devices for monitoring structural health herein utilize pseudo-baseline characteristics to monitor the structural health of a subject. As used herein, pseudo-baseline characteristics refer to reference measurements of the subject being monitored that are captured at any point in time and used for immediate comparison with current characteristics to detect changes or anomalies. Specifically, the pseudo-baseline characteristics are calculated using an average of a plurality of individual characteristics obtained simultaneously, where each individual characteristic represents a guided wave propagating through the subject. When comparing the pseudo-baseline characteristics to a current characteristic, i.e., one of the plurality of individual characteristics, anomalies in the subject can be detected regardless of when the anomaly formed in the subject and regardless of environmental conditions. That is, the pseudo-baseline characteristics of a subject with pre-existing anomalies can be obtained at any point in time and under any of a variety of environmental conditions. The pseudo-baseline characteristics are beneficial over baseline characteristics because they eliminate the need for categorization and retention of baseline characteristics, do not require environmental compensation, and pre-existing damage is detectable and does not invalidate results upon device installation.

[0038] Reference Figure 1The diagram illustrates a device 100 for monitoring the structural health of object 148. Device 100 utilizes guided waves 105, which are mechanical waves propagating along the surface of object 148 or through the structure of object 148. These guided waves 105 exhibit properties such as dispersion, mode switching, and multimode behavior, making them sensitive to various types of defects and structural anomalies in or on object 148. Therefore, guided waves 105 are used to monitor the structural health of object 148. In some instances, guided waves 105 are Lamb waves. Lamb waves are guided waves that propagate through the thickness of the structure, bouncing back and forth between the structural surfaces. Furthermore, Lamb waves are highly sensitive to changes in the material properties of the object and can detect a wide range of anomalies, including cracking, delamination, and corrosion. In other instances, guided waves 105 are surface waves. Surface waves travel along the surface of the object and are particularly effective for detecting large areas. Surface waves are sensitive to surface irregularities and can detect anomalies such as disbond and surface cracking.

[0039] Device 100 is used to identify potential anomalous areas in object 148. As used herein, anomaly refers to any deviation, irregularity, or variation in the structural integrity of the monitored object, including defects, damage, deterioration, abnormalities, or any other indication of structural instability or functional impairment. Anomalies may include any of a variety of forms, such as cracks, delamination, corrosion, deformation, material loss, discontinuities, or changes in mechanical properties. Detecting potential anomalous areas is important for assessing structural condition, identifying potential risks or hazards, and facilitating timely maintenance or intervention to prevent structural weakening or failure.

[0040] To identify potentially anomalous areas, device 100 includes multiple sensor pairs 102 configured to connect to object 148. Device 100 may include any number of sensor pairs 102, including more or fewer. Figure 1 The six sensor pairs shown. In some instances, device 100 may include six sensor pairs, including a first pair 108, a second pair 110, a third pair 112, a fourth pair 114, a fifth pair 116, and an Nth pair 118.

[0041] Each pair of the plurality of sensor pairs 102 includes an excitation sensor 104 and a receiving sensor 106. The excitation sensor 104 is configured to transmit a guided wave 105, while the receiving sensor 106 is configured to receive the guided wave 105 transmitted by the excitation sensor 104. That is, the excitation sensor 104 generates and transmits a guided wave 105 along or through the object 148. When activated, the excitation sensor 104 generates a wave pulse that interacts with the object 148 and potential anomalies of the object 148. In some examples, the excitation sensor 104 utilizes a piezoelectric transducer that is used to generate the guided wave by converting an electrical signal into a mechanical vibration. The characteristics of the guided wave 105, such as the frequency and mode, can be adjusted by the excitation sensor 104 to vary the detection sensitivity of the anomaly.

[0042] The receiving sensor 106 detects the guided wave 105 transmitted by the excitation sensor 104 after the guided wave 105 has interacted with the object 148. Positioned at a predetermined distance from the excitation sensor 104, the receiving sensor 106 captures the wave signal traveling along or through the object 148. The captured signal contains information about the object 148, including any reflections, scatterings, or diffractions caused by an anomaly. The receiving sensor 106 can utilize a piezoelectric element or other sensitive detection mechanism to convert the mechanical vibrations back into an electrical signal, which is then processed to form individual features of the guided wave 105.

[0043] The plurality of sensor pairs 102 are geometrically similar to one another. In some examples, the geometric similarity between each of the plurality of sensor pairs 102 is at least 75%. In other examples, the geometric similarity between each of the plurality of sensor pairs 102 is at least 90%. In still other examples, the geometric similarity between each of the plurality of sensor pairs 102 is at least 95%. Specifically, the plurality of sensor pairs 102 are geometrically similar in the distance (D1) between the excitation sensor 104 and the receiving sensor 106 and the spatial displacement in three-dimensional space (i.e., consistent spatial displacement). That is, the distance (D1) is a fixed separation between the excitation sensor 104 and the receiving sensor 106 within each of the plurality of sensor pairs 102. Further, the spatial displacement includes the three-dimensional arrangement of each of the plurality of sensor pairs 102, ensuring that the relative positions thereof remain consistent along all spatial dimensions (i.e., x, y, and z axes). In order to obtain accurate and meaningful results when comparing the pseudo baseline features to the individual features of a respective one of the plurality of sensor pairs 102, the similarity in distance and consistent spatial displacement is necessary. Further, the underlying structure of the object 148 through which the guided wave 105 travels must be the same to ensure consistent wave propagation characteristics. Thus, if the plurality of sensor pairs 102 are geometrically similar and the underlying structure through which the guided wave 10 travels is the same, the device 100 can be used to evaluate the object 148 for anomalies at any given time.

[0044] Device 100 also includes a processor 120 and a memory 122. In various instances, non-transitory computer-readable instructions (i.e., code) stored in memory 122 (i.e., storage medium) cause processor 120 to monitor the structural health of object 148. Processor 120 (e.g., central processing unit) can be incorporated into various computing devices, such as desktop computers, laptop computers, tablet computers, smartphones, smartwatches, smart TVs, etc. In some instances, a web-based portal can facilitate access to processor 120, allowing device 100 to be used remotely, regardless of its physical location relative to processor 120. Modules for monitoring the structural health of object 148 may include a feature acquisition module 124, a pseudo-baseline generation module 126, a feature comparison module 128, and an anomaly detection module 130.

[0045] The feature acquisition module 124 is configured to acquire individual features from each of the plurality of sensor pairs 102 to generate a plurality of individual features. Each individual feature represents a guided wave 105 received by the receiving sensor 106 of a corresponding pair of the plurality of sensor pairs 102. That is, each of the plurality of sensor pairs 102 generates, transmits, and receives a corresponding guided wave 105, and the feature acquisition module 124 is used to process the raw signal into individual features. Figure 2A As shown, the graphic illustrates multiple individual features 144, each corresponding to one of the multiple sensor pairs 102. The x-axis represents distance, where each point on the x-axis corresponds to a specific location along the guided wave's path, and the y-axis represents the amplitude of the guided wave, indicating the degree to which the signal deviates from its baseline or zero, reflecting the energy of the guided wave as it travels through or along the object 148. The graphic illustrates representative distances and amplitudes of the guided wave, such as distances represented by values ​​between 550 and 900 and amplitudes represented by values ​​between 1500 and -1500. Thus, each individual feature is acquired from one of the multiple sensor pairs 102 and is a representation of the guided wave 105 after propagation on or through the object 148. Specifically, each individual feature represents a characteristic of the received guided wave 105, including any changes caused by structural features or anomalies within or on the object 148. Any changes may manifest as variations in the wave's amplitude, phase, or frequency, which are captured in the electrical signal. For example, the diagram illustrates six individual characteristics: the first individual characteristic 132 corresponds to the first pair 108, the second individual characteristic 134 corresponds to the second pair 110, the third individual characteristic 136 corresponds to the third pair 112, the fourth individual characteristic 138 corresponds to the fourth pair 114, the fifth individual characteristic 140 corresponds to the fifth pair 116, and the Nth individual characteristic 142 corresponds to the Nth pair 118.

[0046] Reference Back Figure 1The pseudo-baseline generation module 126 is configured to generate a pseudo-baseline feature 146 by calculating an average of the plurality of individual features 144. That is, the pseudo-baseline generation module 126 collects the plurality of individual features 144 through the feature acquisition module 124 and processes them to create a representative baseline. Specifically, the pseudo-baseline generation module 126 calculates an average of respective data points across the plurality of individual features 144 to combine the plurality of individual features 144 into an average feature or pseudo-baseline feature 146. As described above, the pseudo-baseline feature 146 can be used as a reference measure of the structural health of the object 148. As shown in FIG. 1, the graphical representation illustrates the pseudo-baseline feature 146 generated by averaging the first individual feature 132, the second individual feature 134, the third individual feature 136, the fourth individual feature 138, the fifth individual feature 140, and the Nth individual feature 142. Figure 2B As shown in FIG. 1, the graphical representation illustrates the pseudo-baseline feature 146 generated by averaging the first individual feature 132, the second individual feature 134, the third individual feature 136, the fourth individual feature 138, the fifth individual feature 140, and the Nth individual feature 142. Figure 2A As shown in FIG. 1, the graphical representation illustrates the pseudo-baseline feature 146 generated by averaging the first individual feature 132, the second individual feature 134, the third individual feature 136, the fourth individual feature 138, the fifth individual feature 140, and the Nth individual feature 142.

[0047] Referring back to FIG. 1, Figure 1 The feature comparison module 128 is configured to compare an individual feature of a respective pair of the plurality of sensor pairs 102 (e.g., the first individual feature 132) to the pseudo-baseline feature 146 to determine whether the individual feature of the respective pair of the plurality of sensor pairs 102 is different from the pseudo-baseline feature 146. In other words, the feature comparison module 128 is used to identify deviations of the individual feature compared to the pseudo-baseline feature 146, which can indicate potential anomalies in the object 148. In some instances, the feature comparison module 128 can measure the degree of deviation between the individual feature and the pseudo-baseline feature to determine whether there is a difference between the features. The comparison can be guided by a predetermined threshold limit, where if the dissimilarity exceeds the threshold limit, the difference between the features exceeds an acceptable limit, and the features are considered different. The predetermined threshold limit can be determined based on factors such as the characteristics of the object 148 or specific requirements of the application.

[0048] In one instance, the device 100 can define an anomaly index based on the degree of deviation between the pseudo-baseline feature 146 and the plurality of individual features 144. The device 100 can also define an individual index based on the degree of deviation between the pseudo-baseline feature 146 and the individual feature of the respective pair of the plurality of sensor pairs 102. The anomaly index and the individual index are numerical values that provide quantitative insights into the level of observed deviation. If the individual index is greater than the anomaly index, the respective pair of the plurality of sensor pairs 102 is different from the pseudo-baseline feature 146.

[0049] When the plurality of sensor pairs 102 are geometrically similar and positioned on a consistent infrastructure of the object 148, the respective individual features of each of the plurality of sensor pairs 102 will be similar, regardless of environmental conditions during data acquisition. Thus, when comparing the individual features to the pseudo-baseline feature 146, any significant difference can be attributed to a potential anomaly. This inference is based on the assumption that, under controlled and consistent settings, changes in individual features are primarily caused by structural anomalies or defects. Thus, detection of such differences enables identification and localization of anomalies within or on the object 148.

[0050] As shown in Figure 2C The graph illustrates a comparison of the pseudo-baseline feature 146 to an individual feature, specifically the first individual feature 132. The graph serves as a visual representation of the degree of deviation between the pseudo-baseline feature 146 and the first individual feature 132. As shown, the first individual feature 132 and the pseudo-baseline feature 146 are not visually aligned at least a portion of the length of the first individual feature 32. To determine whether the first individual feature 132 and the pseudo-baseline feature 146 are different, the difference between the features can be evaluated visually or calculated using a method that determines the degree of deviation, such as statistical analysis or computational algorithms. The threshold at which two features are considered different can vary depending on the specific object or application. Although Figure 2C Only one of the plurality of individual features 144 is shown compared to the pseudo-baseline feature 146, but each of the plurality of individual features will be compared individually to the pseudo-baseline feature 146 to determine whether the individual features are different from the pseudo-baseline feature 146. That is, upon generation of the pseudo-baseline feature 146, it is immediately and individually compared to each of the plurality of individual features of the respective pair of the plurality of sensor pairs. The immediate comparison between the pseudo-baseline feature 146 and the individual features, meaning the comparison is performed without delay, ensures that the pseudo-baseline feature 146 accurately represents the structural health of the object 148 at a given time.

[0051] Referring back to Figure 1If the individual characteristic of a respective pair of the plurality of sensor pairs 102 differs from the pseudo-baseline characteristic 146, the anomaly identification module 130 is configured to identify a region of the object 148 proximate to the respective pair of the plurality of sensor pairs 102 as a potential anomaly region. In some examples, the anomaly identification module 130 can identify a region between the excitation sensor 104 and the receiving sensor 106 of a respective pair of the plurality of sensor pairs 102 as a potential anomaly region. In other examples, the anomaly identification module 130 can identify a specific region between the excitation sensor 104 and the receiving sensor 106 of a respective pair of the plurality of sensor pairs 102 as a potential anomaly region based on an analysis of the guided wave propagation characteristics. In still other examples, the anomaly identification module 130 can identify a potential anomaly region as a section that includes a region between the excitation sensor 104 and the receiving sensor 106 and additional regions extending beyond it. Thus, identifying potential anomaly regions provides targeted information about possible structural issues within a specified region of the object 148, enabling more efficient and focused maintenance efforts. Upon identification, the potential anomaly region can be flagged for further inspection, maintenance, or repair.

[0052] Further, in some examples, if the individual characteristic of a respective pair of the plurality of sensor pairs 102 is similar to the pseudo-baseline characteristic 146, the anomaly identification module 130 can be configured to identify a region of the object 148 proximate to the respective pair of the plurality of sensor pairs 102 as a structurally normal region. Structurally normal regions are not flagged for further inspection, maintenance, or repair at a given time.

[0053] In some instances, the plurality of sensor pairs 102 are configured to operate in a continuous monitoring mode. That is, the processor 120 continuously acquires updated individual features from each of the plurality of sensor pairs 102, generates updated pseudo-baseline features, and compares the updated individual features to the updated pseudo-baseline features to provide real-time detection and identification of potential abnormal regions. If at any given time the updated individual features are found to be different from the updated pseudo-baseline features, the device 100 identifies the region of the object 148 proximate to the corresponding one of the plurality of sensor pairs 102 as a potential abnormal region. Continuous monitoring allows for real-time identification of potential abnormal regions, allowing for immediate knowledge of any structural changes in the monitored region of the object 148. In other instances, the plurality of sensor pairs 102 are configured to operate in a periodic monitoring mode. That is, the processor 120 periodically acquires updated individual features from each of the plurality of sensor pairs 102, generates updated pseudo-baseline features, and compares the updated individual features to the updated pseudo-baseline features to provide monitoring at specific times to detect and identify potential abnormal regions. Periodic monitoring ensures structural health oversight at specific times, such as specific maintenance intervals, facilitating efficient resource allocation and maintenance scheduling.

[0054] Figure 3A and 3B A structural health monitoring system 200 is shown. The structural health monitoring system 200 is used to monitor the structural health of an object 148 (shown in cross-sectional view), wherein one of the plurality of sensor pairs 102 is shown coupled to the object 148. As described above, the object 148 includes the plurality of sensor pairs 102 coupled to the object 148 in a geometrically similar manner. While the structural health monitoring system 200 will be described using a representative one of the plurality of sensor pairs (the first pair 108), it is important to note that the object 148 can include any number of sensor pairs, each of which will operate in the same manner. In some instances, the plurality of sensor pairs 102 are positioned on a surface of the object 148. For example, the first pair 108 is positioned on a surface 152 of the object 148. The sensors can be positioned on a surface of the object for reasons such as accessibility, non-invasive monitoring, enhanced sensitivity, any other purpose. In other instances, the plurality of sensor pairs 102 are embedded within a structure 150 of the object 148 for reasons such as enhanced structural integration, prevention of external elements, or other specific requirements. That is, the plurality of sensor pairs 102 are integrated into the structure of the object 148 - during its manufacturing process, or inserted into the structure - in a manner that it becomes an integral part of the structure.

[0055] In particular, the excitation sensor 104 of the first pair 108 transmits a guided wave 105 that propagates within or along a structure 150 of the object 148 in a propagation direction 154. In one example, the guided wave 105 propagates through a thickness (T) of the object 148 in the propagation direction 154 parallel to a surface 152 of the object 148. That is, the guided wave 105 propagates back and forth between a top surface 152 and a bottom surface of the object 148. In other examples, the guided wave 105 propagates along the surface 152 of the object 148, moving in a circular or elliptical motion from the excitation sensor 104. The receiving sensor 106 receives the guided wave 105 after it has propagated within or along the object 148. The guided wave 105 received by the receiving sensor 106 is used by the processor 120 and the memory 122 to generate a first individual feature 132 for the first pair 108. The first individual feature 132 is compared to a pseudo-baseline feature 146, which is generated by calculating an average of the plurality of individual features 144.

[0056] The structural health monitoring system 200 is used to identify the presence or absence of anomalies at a structure health state that is unknown. Referring to Figure 3A , the object 148 exhibits no anomalies on or within the object 148. In comparison, the first individual feature 132 is similar to the pseudo-baseline feature 146, indicating structural integrity. Accordingly, the region 156 proximate to the first pair 108 is identified as a structurally normal region 160. In contrast, referring to Figure 3B , the object 148 exhibits an anomaly 151 within the object 148. In comparison, the first individual feature 132 is different from the pseudo-baseline feature 146, indicating an anomaly. Accordingly, the region 156 proximate to the first pair 108 is identified as a potentially anomalous region 158. The object 148 can be flagged for inspection, maintenance, or repair to discover and address the previously unknown anomaly 151.

[0057] The structural health monitoring system 200 can be used to monitor any object for which monitoring structural health is beneficial. For example, the structural health monitoring system 200 can be used to monitor the structural integrity of a bridge or building, for aerospace to ensure the safety of an aircraft structure, and for the automotive industry to monitor vehicle components, among other applications. In some examples, the structural health monitoring system 200 is used to monitor for anomalies in an aircraft 166. The structural health monitoring system 200 can be used to monitor structural health with respect to the overall integrity of the object 148, such that the plurality of pairs of sensors are positioned throughout the object 148. In contrast, the structural health monitoring system 200 can be used to target specific regions on the object 148, such as regions that are prone to anomalies or that are considered important for structural integrity. For example, the plurality of pairs of sensors 102 can be coupled to a local region 168 of the object 148.

[0058] In some instances, the plurality of sensor pairs 102 are coupled to or positioned near a component of object 148. Coupling or positioning the plurality of sensor pairs 102 near a component of object 148 can help ensure that the sensor pairs are geometrically similar to each other. For example, as... Figure 4 As shown, the plurality of sensor pairs 102 are coupled to a plurality of rivet pairs 164 on object 148. Individual rivets 162 in each of the plurality of rivet pairs 164 are equidistant from each other, such that the individual rivets 162 in each rivet pair 164 are spaced apart by a distance D2. As shown, the plurality of sensor pairs 102 are coupled to the plurality of rivet pairs such that the first pair 108, the second pair 110, the third pair 112, the fourth pair 114, the fifth pair 116, and the Nth pair 118 are each coupled to a corresponding pair of the plurality of rivet pairs 164. Other groups of sensor pairs 102 may correspond to other local areas 168 of object 148, such as other groups of rivet pairs, allowing multiple areas to be monitored by the corresponding structural health monitoring system 200.

[0059] refer to Figure 5 Based on some examples, a method 300 for monitoring the structural health of an object 148 is shown. Method 300 includes the step of acquiring individual features from each of a plurality of sensor pairs 102 to generate a plurality of individual features 144 (block 302). Each individual feature represents a guided wave 105 transmitted by an excitation sensor 104 of a corresponding pair of the plurality of sensor pairs and received by a receiving sensor 106. The guided wave 105 propagates through or along the object 148, capturing information about the object's structural health, such that the individual features can be used to identify any discrepancies that may indicate potential structural anomalies.

[0060] Method 300 further includes the step of generating a pseudo-baseline feature 146 by calculating the average of the plurality of individual features 144 (block 304). The pseudo-baseline feature 146 serves as a reference metric representing the expected normal condition of the object 148. By averaging the individual features 144, the pseudo-baseline feature 146 minimizes the effect of any random variation or noise, providing a standard by which individual features can be compared to detect anomalies.

[0061] Method 300 further includes the step (block 306): comparing the individual characteristics of a corresponding pair of the plurality of sensor pairs 102 with a pseudo-baseline feature 146 to determine whether the individual characteristics of the corresponding pair of the plurality of sensor pairs 102 differ from the pseudo-baseline feature 146. This comparison involves analyzing any deviation of the individual characteristics relative to the pseudo-baseline feature 146. The difference between these two features can indicate potential structural anomalies in the monitored area by the corresponding sensor pair. The thresholds for treating these two features differently can be varied depending on the specific object or application.

[0062] If the individual characteristic of the respective pair of the plurality of sensor pairs 102 is different than the pseudo-baseline characteristic 146, the method 300 includes the step (block 308) of identifying the region of the object 148 proximate to the respective pair of the plurality of sensor pairs 102 as a potential abnormal region 158. That is, the method 300 can be used to identify a specific region of the object 148 as a potential abnormal region. This targeted identification allows for focused inspection and timely maintenance to address any detected structural issues.

[0063] In some examples, the method 300 includes defining an abnormality index based on the degree of deviation between the pseudo-baseline characteristic 146 and the plurality of individual characteristics 144, and defining an individual index based on the degree of deviation between the pseudo-baseline characteristic 146 and the individual characteristic of the respective pair of the plurality of sensor pairs. If the individual index is greater than the abnormality index, the respective pair of the plurality of sensor pairs 102 is different than the pseudo-baseline characteristic 146. Accordingly, the region 156 of the object 148 proximate to the respective pair of the plurality of sensor pairs 102 is identified as a potential abnormal region 158.

[0064] In some examples, the method 300 is configured to operate in a periodic monitoring mode. In the periodic monitoring mode, the individual characteristic of each pair of the plurality of sensor pairs 102 is acquired at specific monitoring times and compared to the pseudo-baseline characteristic 146 to detect and identify potential abnormal regions. In other examples, the method 300 is configured to operate in a continuous monitoring mode. In the continuous monitoring mode, the method continuously acquires updated individual characteristics from each pair of the plurality of sensor pairs 102, generates updated pseudo-baseline characteristics, and compares the updated individual characteristics to the updated pseudo-baseline characteristics to enable real-time detection and identification of potential abnormal regions.

[0065] In the foregoing description, certain terminology has been used to describe certain features, such as "upper," "lower," "upper portion," "lower portion," "horizontal," "vertical," "left," "right," "above," "below," and the like. Such terminology with respect to the features is used, where applicable, to provide some description clarity. This is for purposes of the specification only and is not intended to limit the scope of the concepts in any way. For example, terms such as "upper," "lower," "up," "down," and the like are used in relation to the object, by way of example only. The object can be flipped over into its upside down position and the described components can then be described as an "upper" surface versus a "lower" surface. Or, stated another way, the

[0066] Furthermore, where an element or layer is described as being "on," "connected to," "coupled to" or "mounted" on another element or layer, it can be directly on, connected, coupled or mounted to the other element or layer, or intervening elements or layers can be present. In addition, as used herein, the term "connected" can include direct and indirect connections. For example, if a first element is "connected to" a second element, then the first element can be directly connected to the second element or one or more intervening elements can be present. In addition, as used herein, the term "on" does not necessarily mean "directly on," unless otherwise indicated. For example, one element can be "on" another element without being in direct contact with the other element.

[0067] As used herein, the phrase "at least one of" followed by a listing of two or more items means that any of the listed items can be utilized individually, or any combination of the listed items can be utilized. The phrase "at least one of" is intended to cover the selection of one or more of the items from the group consisting of the items recited. For instance, "at least one of A, B, and C" is intended to cover A alone; B alone; C alone; A and B; A and C; B and C; or A, B, and C. In addition, the phrase "at least one of" is intended to cover the selection of one or more of the items from the group consisting of the items recited, where the number of items in the selection is not limited to the number of items recited. For instance, "at least one of A, B, and C" is intended to cover the selection of two of A, B, and C, such as two A's and C; or A and two B's, etc.

[0068] Unless otherwise noted, the terms "first," "second," etc. are used herein only to distinguish one element from another, and do not imply a required or a prioritized order of elements to or in operation with each other. Further, with respect to an element recited as, for example, a "second" item, there is no requirement or exclusion that there be a "first" or lower-numbered item, and / or a "third" or higher-numbered item.

[0069] As used herein, a system, device, structure, article, element, component, or hardware that is "configured to" perform a particular function is capable of performing the specified function without any alteration, rather than merely having the potential to perform the specified function after a modification, for example, a modification affecting a different function. In other words, a system, device, structure, article, element, component, or hardware that is "configured to" perform a particular function is specifically selected, created, programmed, and / or designed for the purpose of performing that function. As used herein, "configured to" denotes existing characteristics of a system, device, structure, article, element, component, or hardware that make the system, device, structure, article, element, component, or hardware capable of performing the specified function without further modification. For purposes of the present disclosure, a system, device, structure, article, element, component, or hardware described as being "configured to" perform a particular function can additionally or alternatively be described as being "arranged to" and / or "operative to" perform that function.

[0070] In some embodiments, the term "about" or "substantially" is defined as being within + / - 5% of a given value, however in other embodiments any disclosed "about" can be further narrowed and intended to mean within + / - 4% of a given value, within + / - 3% of a given value, within + / - 2% of a given value, within + / - 1% of a given value, or the exact given value. Further, when at least two values of a variable are disclosed, such disclosure specifically intends to include the range between the two values, whether or not it is disclosed with respect to separate embodiments or examples, and specifically intends to include the range of at least the smaller of the two values and / or no more than the larger of the two values. Further, when at least three values of a variable are disclosed, such disclosure specifically intends to include the range between any two of the values, whether or not it is disclosed with respect to separate embodiments or examples, and specifically intends to include the range of at least the value A and / or no more than the value B, where A can be any one of the non-maximum disclosed values and B can be any one of the non-minimum disclosed values.

[0071] The illustrative flow diagrams contained herein generally present the flow in a logical flow diagram. Consequently, the depicted order and labeled steps are indicative of one example of the method shown. Other steps and methods can be conceived that are functionally, logically, or otherwise equivalent to one or more steps or portions thereof of the described methods. Further, the described format and symbols are provided to explain the logical steps of the methods and are understood but not limiting of the scope of the methods. Although various arrow types and line types can be employed in the flow diagram, they are understood not to limit the scope of the corresponding methods. Indeed, some arrows or other connectors can be used to indicate certain examples of the logical flow of a method. For instance, an arrow can indicate a waiting or monitoring period of unspecified duration between enumerated steps of the described methods. Additionally, the order in which a particular method occurs can or can not strictly adhere to the order of the steps shown.

[0072] The functional units described in this specification have been labeled as modules, in order to more particularly emphasize their implementation independence. For example, a module can be implemented as a hardware circuit comprising custom VLSI circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module can also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like.

[0073] Modules can also be implemented in code and / or software for execution by various types of processors. An identified module of code may, for instance, include one or more physical or logical blocks of executable code which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together, but can include disparate instructions stored in different locations which, when joined logically together, comprise the module and achieve the stated purpose for the module.

[0074] Indeed, a module of code can be a single instruction, or many instructions, and can even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data can be identified and illustrated herein within modules, and can be embodied in any suitable form and organized within any suitable type of data structure. The operational data can be collected as a single data set, or can be distributed over different locations including over different computer readable storage devices. Where a module or portions of a module are implemented in software, the software portion is stored in one or more computer readable storage devices.

[0075] Any combination of one or more computer readable medium can be utilized. The computer readable medium can be a computer readable storage medium. The computer readable storage medium can be a storage device storing the code. The storage device can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.

[0076] More specific examples (a non-exhaustive list) of the storage device would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium can be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0077] Code for carrying out operations can be written in any combination of one or more programming languages, including an object oriented programming language such as Python, Ruby, Java, Smalltalk, C++, or the like, and conventional procedural programming languages, such as the "C" programming language, or the like, and / or machine languages such as assembly languages. The code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the

[0078] The described features, structures, or characteristics of the examples can be combined in any suitable manner. In the above description, numerous specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a thorough understanding of examples. One skilled in the relevant art will recognize, however, that the examples can be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail in order to avoid obscuring aspects of the examples.

[0079] The above description of illustrated examples of the present application, including what is described in the abstract, is not intended to be exhaustive or to be construed as excluding any

[0080] The code can also be stored in a storage device that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the storage device produce an article of manufacture including instructions which implement the function / act specified in the schematic flowchart diagrams and / or schematic block diagrams block or blocks.

[0081] The code can also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the code which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0082] The flowchart and / or block diagram in the figure illustrate the architecture, functionality, and operation of possible implementations of devices, systems, methods and computer program products according to various examples. In this regard, each block in the flowchart and / or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions implementing the specified logical functions (s).

[0083] The subject matter can be implemented in additional forms without departing from its spirit or essential characteristics. The described implementations are to be considered in all respects only as illustrative and not restrictive. Changes in the meaning of equivalents are included in the scope of the claims.

[0084] Exemplary technical solutions

[0085] 1. A device (100) for monitoring structural health of an object (148), the device (100) comprising:

[0086] a plurality of sensor pairs (102) configured to be coupled to the object (148), wherein each of the plurality of sensor pairs (102) comprises an excitation sensor (104) and a receiving sensor (106), and wherein:

[0087] the excitation sensor (104) is configured to transmit a guided wave (105);

[0088] the receiving sensor (106) is configured to receive the guided wave (105) transmitted by the excitation sensor (104); and

[0089] the plurality of sensor pairs (102) are geometrically similar to each other;

[0090] a processor (120); and

[0091] a memory (122) storing code executable by the processor (120) for:

[0092] acquiring an individual signature from each of the plurality of sensor pairs (102) to generate a plurality of individual signatures

[0093] (144), wherein each individual signature represents the guided wave (105) received by the receiving sensor (106) of a respective one of the plurality of sensor pairs (102);

[0094] generating a pseudo-baseline signature (146) by calculating a mean of the plurality of individual signatures (144);

[0095] comparing the individual signature of a respective one of the plurality of sensor pairs (102) to the pseudo-baseline signature (146) to determine whether the individual signature of the respective one of the plurality of sensor pairs (102) is different from the pseudo-baseline signature (146); and

[0096] identifying a region (156) of the object (148) proximate to the respective one of the plurality of sensor pairs (102) as a potential anomalous region (158) if the individual signature of the respective one of the plurality of sensor pairs (102) is different.

[0097] 2. The apparatus (100) of claim 1, wherein the guided wave (105) transmitted by the excitation sensor (104) of each of the plurality of sensor pairs (102) is a Lamb wave.

[0098] 3. The apparatus (100) of claim 1, wherein the guided wave (105) transmitted by the excitation sensor (104) of each of the plurality of sensor pairs (102) is a surface wave.

[0099] 4. The apparatus (100) of claim 1, wherein the pseudo-baseline signature (146) is compared to the individual signature of the respective one of the plurality of sensor pairs (102) immediately after the pseudo-baseline signature (146) is generated.

[0100] 5. The apparatus (100) of claim 1, wherein geometric similarity of each of the plurality of sensor pairs (102) comprises:

[0101] a distance (Dl) between the excitation sensor (104) and the receiving sensor (106) of each of the plurality of sensor pairs (102) is the same; and

[0102] The spatial displacement in three-dimensional space between the excitation sensor (104) and the receiving sensor (106) of each of the plurality of sensor pairs (102) is consistent.

[0103] 6. The apparatus (100) of claim 1, wherein the memory (122) further stores code executable by the processor (120) for:

[0104] defining an anomaly index based on a degree of deviation between the pseudo-baseline signature (146) and the plurality of individual signatures (144); and

[0105] defining an individual index based on a degree of deviation between the pseudo-baseline signature (146) and the individual signature of the respective one of the plurality of sensor pairs (102), wherein:

[0106] the anomaly index and the individual index are numerical values; and

[0107] if the individual index is greater than the anomaly index, the respective one of the plurality of sensor pairs (102) is different from the pseudo-baseline signature (146) such that the region (156) of the object (148) proximate to the respective one of the plurality of sensor pairs is identified as the potential abnormal region (158).

[0108] 7. The apparatus (100) of claim 1, wherein if the individual signature of the respective one of the plurality of sensor pairs (102) is similar to the pseudo-baseline signature (146), the region (156) of the object (148) proximate to the respective one of the plurality of sensor pairs (102) is identified as a structurally normal region (160).

[0109] 8. The apparatus (100) of claim 1, wherein:

[0110] the plurality of sensor pairs (102) are configured to operate in a continuous monitoring mode; and

[0111] the processor (120) continuously obtains updated individual signatures from each of the plurality of sensor pairs (102), generates an updated pseudo-baseline signature, and compares the updated individual signatures from each of the plurality of sensor pairs (102) to the updated pseudo-baseline signature (146) to provide real-time detection and identification of the potential abnormal region.

[0112] 9. A structural health monitoring system (200), comprising:

[0113] an object (148) whose structural health is to be monitored;

[0114] a plurality of sensor pairs (102) coupled to the object (148), wherein each of the plurality of sensor pairs (102) includes an excitation sensor (104) and a receiving sensor (106), and wherein:

[0115] the excitation sensor (104) is configured to transmit a guided wave (105);

[0116] the receiving sensor (106) is configured to receive the guided wave (105) transmitted by the excitation sensor (104);

[0117] the plurality of sensor pairs (102) are geometrically similar to one another; and

[0118] a structure (150) of the object (148) through which the guided wave (105) of each of the plurality of sensor pairs (102) propagates is the same;

[0119] a processor (120); and

[0120] a memory (122) storing code executable by the processor (120) for:

[0121] obtaining an individual feature from each of the plurality of sensor pairs (102) to generate a plurality of individual features (144), wherein each individual feature represents the guided wave (105) received by the receiving sensor (106) of a respective one of the plurality of sensor pairs (102);

[0122] generating a pseudo-baseline feature (146) by computing an average of the plurality of individual features (144);

[0123] comparing the individual feature of a respective one of the plurality of sensor pairs (102) to the pseudo-baseline feature (146) to determine whether the individual feature of the respective one of the plurality of sensor pairs (102) differs from the pseudo-baseline feature (146); and

[0124] if the individual feature of the respective one of the plurality of sensor pairs (102) differs, identifying a region (156) of the object (148) proximate to the respective one of the plurality of sensor pairs (102) as a potential anomalous region (158).

[0125] 10. The structural health monitoring system (200) of claim 9, wherein the guided wave (105) transmitted by the excitation sensor (104) of each of the plurality of sensor pairs (102) is configured to propagate through a thickness (T) of the object (148) in a propagation direction (154) parallel to a surface (152) of the object (148).

[0126] 11. The structural health monitoring system (200) of claim 9, wherein the guided wave (105) transmitted by the excitation sensor (104) of each of the plurality of sensor pairs (102) is configured to propagate along a surface (152) of the object (148) moving in a circular or elliptical motion from the excitation sensor (104).

[0127] 12. The structural health monitoring system (200) of claim 9, wherein the plurality of sensor pairs (102) are positioned on a surface (152) of the structure (150) of the object (148).

[0128] 13. The structural health monitoring system (200) of claim 9, wherein the plurality of sensor pairs (102) are embedded within the structure (150) of the object (148).

[0129] 14. The structural health monitoring system (200) of claim 9, wherein the plurality of sensor pairs (102) are coupled to or positioned proximate to a plurality of rivet pairs (164) on the object (148), wherein individual rivets (162) of each of the plurality of rivet pairs (164) are equally spaced apart from one another.

[0130] 15. The structural health monitoring system (200) of claim 9, wherein the object (148) to be monitored for anomalies is an aircraft (166).

[0131] 16. The structural health monitoring system (200) of claim 9, wherein the plurality of sensor pairs (102) are coupled to a local region (168) of the object (148), wherein the local region (168) of the object (148) corresponds to a region of the object (148) prone to anomalies.

[0132] 17. A method (300) of monitoring the structural health of an object (148), the method (300) comprising:

[0133] acquiring (302) an individual signature from each of a plurality of sensor pairs (102) to generate a plurality of individual signatures (144), wherein each individual signature represents a guided wave (105) transmitted by an excitation sensor (104) of a respective one of the plurality of sensor pairs (102) and received by a receiving sensor (106);

[0134] generating (304) a pseudo-baseline signature (146) by calculating an average of the plurality of individual signatures (144);

[0135] comparing (306) the individual signature of the respective one of the plurality of sensor pairs (102) to the pseudo-baseline signature (146) to determine whether the individual signature of the respective one of the plurality of sensor pairs (102) is different from the pseudo-baseline signature (146); and

[0136] identifying (308) a region (156) of the object (148) proximate to the respective one of the plurality of sensor pairs (102) as a potential abnormal region (158) if the individual signature of the respective one of the plurality of sensor pairs (102) is different.

[0137] 18. The method (300) of technical solution 17, further comprising:

[0138] defining an anomaly index based on a degree of deviation between the pseudo-baseline signature (146) and the plurality of individual signatures (144); and

[0139] defining an individual index based on a degree of deviation between the pseudo-baseline signature (146) and the individual signature (144) of the respective one of the plurality of sensor pairs (102);

[0140] wherein the respective one of the plurality of sensor pairs (102) is different from the pseudo-baseline signature (146) if the individual index is greater than the anomaly index, such that the region (156) of the object (148) proximate to the respective one of the plurality of sensor pairs (102) is identified as the potential abnormal region (158).

[0141] 19. The method (300) of technical solution 17, wherein the plurality of sensor pairs (102) are configured to operate in a periodic monitoring mode such that the individual signature from each of the plurality of sensor pairs (102) is acquired at a specific monitoring time and compared to the pseudo-baseline signature (146) to detect and identify potential abnormal regions.

[0142] 20. The method (300) of claim 17, wherein the plurality of sensor pairs (102) are configured to operate in a continuous monitoring mode, continuously acquiring updated individual features from each of the plurality of sensor pairs (102), generating updated pseudo-baseline features, and comparing the updated individual features from each of the plurality of sensor pairs (102) to the updated pseudo-baseline features, thereby enabling real-time detection and identification of the potential abnormal regions.

Claims

1. An apparatus (100) for monitoring structural health of a subject (148), the apparatus (100) comprising: a plurality of sensor pairs (102) configured to be coupled to the subject (148), wherein each of the plurality of sensor pairs (102) includes an excitation sensor (104) and a receiving sensor (106), and wherein: the excitation sensor (104) is configured to transmit a guided wave (105); the receiving sensor (106) is configured to receive the guided wave (105) transmitted by the excitation sensor (104); and the plurality of sensor pairs (102) are geometrically similar to each other; a processor (120); and a memory (122) storing code executable by the processor (120) for: obtaining an individual feature from each of the plurality of sensor pairs (102) to generate a plurality of individual features (144), wherein each individual feature represents the guided wave (105) received by the receiving sensor (106) of a respective one of the plurality of sensor pairs (102); generating a pseudo-baseline feature (146) by computing an average of the plurality of individual features (144); comparing the individual feature of a respective one of the plurality of sensor pairs (102) to the pseudo-baseline feature (146) to determine whether the individual feature of the respective one of the plurality of sensor pairs (102) is different from the pseudo-baseline feature (146); and identifying a region (156) of the subject (148) proximate to the respective one of the plurality of sensor pairs (102) as a potential anomalous region (158) if the individual feature of the respective one of the plurality of sensor pairs (102) is different.

2. The apparatus (100) of claim 1, wherein the guided wave (105) transmitted by the excitation sensor (104) of each of the plurality of sensor pairs (102) is a Lamb wave.

3. The apparatus (100) of claim 1, wherein the guided wave (105) transmitted by the excitation sensor (104) of each of the plurality of sensor pairs (102) is a surface wave.

4. The apparatus (100) of claim 1, wherein the pseudo-baseline feature (146) is compared to the individual feature of the respective one of the plurality of sensor pairs (102) immediately after the pseudo-baseline feature (146) is generated.

5. The apparatus (100) of claim 1, wherein the geometric similarity of each of the plurality of sensor pairs (102) includes: a distance (Dl) between the excitation sensor (104) and the receiving sensor (106) of each of the plurality of sensor pairs (102) is the same; and a spatial displacement in three-dimensional space between the excitation sensor (104) and the receiving sensor (106) of each of the plurality of sensor pairs (102) is consistent.

6. The apparatus (100) of claim 1, wherein the memory (122) further stores code executable by the processor (120) for: defining an anomaly index based on a degree of deviation between the pseudo baseline signature (146) and the plurality of individual signatures (144); and defining an individual index based on a degree of deviation between the pseudo baseline signature (146) and the individual signature of the respective one of the plurality of sensor pairs (102), wherein: the anomaly index and the individual index are numerical values; and if the individual index is greater than the anomaly index, the respective one of the plurality of sensor pairs (102) is different from the pseudo baseline signature (146) such that the region (156) of the object (148) proximate the respective one of the plurality of sensor pairs is identified as the potential abnormal region (158).

7. The device (100) according to claim 1, wherein if the individual signature of the respective one of the plurality of sensor pairs (102) is similar to the pseudo baseline signature (146), the region (156) of the object (148) proximate the respective one of the plurality of sensor pairs (102) is identified as a structurally normal region (160).

8. The apparatus (100) of claim 1, wherein: the plurality of sensor pairs (102) are configured to operate in a continuous monitoring mode; and the processor (120) continuously acquires updated individual signatures from each of the plurality of sensor pairs (102), generates an updated pseudo baseline signature, and compares the updated individual signatures from each of the plurality of sensor pairs (102) to the updated pseudo baseline signature (146) to provide real-time detection and identification of the potential abnormal region.

9. A structural health monitoring system (200) comprising: an object (148) whose structural health is to be monitored; a plurality of sensor pairs (102) coupled to the object (148), wherein each of the plurality of sensor pairs (102) includes an excitation sensor (104) and a receiving sensor (106), and wherein: the excitation sensor (104) is configured to transmit a guided wave (105); the receiving sensor (106) is configured to receive the guided wave (105) transmitted by the excitation sensor (104); the plurality of sensor pairs (102) are geometrically similar to each other; and a structure (150) of the object (148) through which the guided wave (105) of each of the plurality of sensor pairs (102) propagates is the same; a processor (120); and a memory (122) storing code executable by the processor (120) for: acquiring an individual signature from each of the plurality of sensor pairs (102) to generate a plurality of individual signatures (144), wherein each individual signature represents the guided wave (105) received by the receiving sensor (106) of a respective one of the plurality of sensor pairs (102); generating a pseudo-baseline feature (146) by calculating an average of the individual features (144) of the plurality of individuals; comparing the individual feature of a respective pair of the plurality of sensor pairs (102) to the pseudo-baseline feature (146) to determine whether the individual feature of the respective pair of the plurality of sensor pairs (102) differs from the pseudo-baseline feature (146); and identifying a region (156) of the object (148) proximate to the respective pair of the plurality of sensor pairs (102) as a potential abnormal region (158) if the individual feature of the respective pair of the plurality of sensor pairs (102) differs.

10. The structural health monitoring system (200) of claim 9, wherein the guided wave (105) transmitted by the excitation sensor (104) of each of the plurality of sensor pairs (102) is configured to propagate through a thickness (T) of the object in a propagation direction (154) parallel to a surface (152) of the object (148).

Images