SYSTEMS AND METHODS FOR VEHICLE FAULT DETECTION AND IDENTIFICATION USING AUDIO ANALYSIS

A method for automatically determining a fault of a vehicle comprises receiving one or more audio signals from one or more microphones of the vehicle; extracting diagnostic metadata from the received one or more audio signals; extracting a diagnostic feature from the diagnostic metadata, the extracted diagnostic feature corresponding to a feature of a trained machine-learning based model for determining a fault based on a learned association between the extracted diagnostic feature and a fault of the vehicle; and automatically determining the fault based on the extracted diagnostic feature, by using the trained machine-learning based model that was trained based on a first feature extracted from first training metadata regarding previously recorded data and a second feature extracted from metadata regarding a previous fault related to the previously recorded data, based on the learned association between the extracted diagnostic feature and the fault.

TECHNICAL FIELD

Various embodiments of the present disclosure relate generally to systems and methods for detecting and identifying a fault of a vehicle based on an audio signal, and, more particularly, to systems and methods for using artificial intelligence to analyze an audio signal to detect and identify a fault of a vehicle.

BACKGROUND

A fault may occur on a vehicle, such as a motor vehicle or an aircraft, for example, which provides no indication to the operator of the cause of the fault. The fault may be accompanied by a sudden and irreproducible sound. This sound may provide useful information for diagnosing and addressing the issue. However, without experienced personnel, such as a maintenance crew, for example, in the vehicle at the time that the sound is produced by the vehicle, the issue may be difficult to diagnose.

SUMMARY OF THE DISCLOSURE

In some aspects, the techniques described herein relate to a method for automatically determining a fault of a vehicle, the method including: receiving one or more audio signals from one or more microphones of the vehicle; extracting diagnostic metadata from the received one or more audio signals; extracting a diagnostic feature from the diagnostic metadata, the extracted diagnostic feature corresponding to a feature of a trained machine-learning based model for determining a fault based on a learned association between the extracted diagnostic feature and a fault of the vehicle; and automatically determining the fault based on the extracted diagnostic feature, by using the trained machine-learning based model that was trained based on a first feature extracted from first training metadata regarding previously recorded data and a second feature extracted from metadata regarding a previous fault related to the previously recorded data, based on the learned association between the extracted diagnostic feature and the fault.

In some aspects, the techniques described herein relate to a method, further including: receiving an input signal from a user input of the vehicle to create a timestamp for the received one or more audio signals; and triangulating a location of a source of the received one or more audio signals using the timestamp and the received one or more audio signals.

In some aspects, the techniques described herein relate to a method, further including: storing the received one or more audio signals and the timestamp.

In some aspects, the techniques described herein relate to a method, further including: storing vehicle data associated with the received one or more audio signals and the timestamp.

In some aspects, the techniques described herein relate to a method, wherein the vehicle is an aircraft, the one or more microphones of the vehicle includes a headset in a cockpit of the aircraft, and the vehicle data is avionics data.

In some aspects, the techniques described herein relate to a method, wherein: the one or more microphones includes a first microphone and a second microphone, the one or more audio signals includes a first audio signal from the first microphone and a second audio signal from the second microphone, and the triangulating the location of the source includes using the first audio signal and the second audio signal.

In some aspects, the techniques described herein relate to a method, wherein the extracting the diagnostic metadata from the received one or more audio signals includes using natural language processing.

In some aspects, the techniques described herein relate to a method, further including: providing an alert to an operator of the vehicle based on the determined fault.

In some aspects, the techniques described herein relate to a method, wherein the extracting the diagnostic metadata from the received one or more audio signals includes reducing constant noise patterns in the received one or more audio signals using an active noise cancellation filter.

In some aspects, the techniques described herein relate to a method, further including: temporarily storing a rolling predetermined amount of the received one or more audio signals.

In some aspects, the techniques described herein relate to a system for automatically determining a fault of a vehicle, the system including: one or more processors configured to perform operations including: receiving one or more audio signals from one or more microphones of the vehicle; extracting diagnostic metadata from the received one or more audio signals; extracting a diagnostic feature from the diagnostic metadata, the extracted diagnostic feature corresponding to a feature of a trained machine-learning based model for determining a fault based on a learned association between the extracted diagnostic feature and a fault of the vehicle; and automatically determining the fault based on the extracted diagnostic feature, by using the trained machine-learning based model that was trained based on a first feature extracted from first training metadata regarding previously recorded data and a second feature extracted from metadata regarding a previous fault related to the previously recorded data, based on the learned association between the extracted diagnostic feature and the fault.

In some aspects, the techniques described herein relate to a system, wherein the operations further include: receiving an input signal from a user input of the vehicle to create a timestamp for the received one or more audio signals; and triangulating a location of a source of the received one or more audio signals using the timestamp and the received one or more audio signals.

In some aspects, the techniques described herein relate to a system, wherein the operations further include: storing the received one or more audio signals and the timestamp.

In some aspects, the techniques described herein relate to a system, wherein the operations further include: storing vehicle data associated with the received one or more audio signals and the timestamp.

In some aspects, the techniques described herein relate to a system, wherein the vehicle is an aircraft, the one or more microphones of the vehicle includes a headset in a cockpit of the aircraft, and the vehicle data is avionics data.

In some aspects, the techniques described herein relate to a system, wherein: the one or more microphones includes a first microphone and a second microphone, the one or more audio signals includes a first audio signal from the first microphone and a second audio signal from the second microphone, and the triangulating the location of the source includes using the first audio signal and the second audio signal.

In some aspects, the techniques described herein relate to a system, wherein the extracting the diagnostic metadata from the received one or more audio signals includes using natural language processing.

In some aspects, the techniques described herein relate to a system, wherein the operations further include: providing an alert to an operator of the vehicle based on the determined fault.

In some aspects, the techniques described herein relate to a system, wherein the extracting the diagnostic metadata from the received one or more audio signals includes reducing constant noise patterns in the received one or more audio signals using an active noise cancellation filter.

In some aspects, the techniques described herein relate to a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for automatically determining a fault of a vehicle, the operations including: receiving one or more audio signals from one or more microphones of the vehicle; extracting diagnostic metadata from the received one or more audio signals; extracting a diagnostic feature from the diagnostic metadata, the extracted diagnostic feature corresponding to a feature of a trained machine-learning based model for determining a fault based on a learned association between the extracted diagnostic feature and a fault of the vehicle;

and automatically determining the fault based on the extracted diagnostic feature, by using the trained machine-learning based model that was trained based on a first feature extracted from first training metadata regarding previously recorded data and a second feature extracted from metadata regarding a previous fault related to the previously recorded data, based on the learned association between the extracted diagnostic feature and the fault.

DETAILED DESCRIPTION OF EMBODIMENTS

Both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the features, as claimed. As used herein, the terms “comprises,” “comprising,” “has,” “having,” “includes,” “including,” or other variations thereof, are intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements, but may include other elements not expressly listed or inherent to such a process, method, article, or apparatus. In this disclosure, unless stated otherwise, relative terms, such as, for example, “about,” “substantially,” and “approximately” are used to indicate a possible variation of ±10% in the stated value. In this disclosure, unless stated otherwise, any numeric value may include a possible variation of ±10% in the stated value. In this disclosure, unless stated otherwise, “automatically” is used to indicate that an operation is performed without user input or intervention.

Various embodiments of the present disclosure relate generally to systems and methods for detecting and identifying a fault of a vehicle based on an audio signal, and, more particularly, to systems and methods for using artificial intelligence to analyze an audio signal to detect and identify a fault of a vehicle.

A fault may occur on a vehicle, such as a motor vehicle or an aircraft, for example, which provides no indication to the operator of the cause of the fault. The fault may be accompanied by a sudden and irreproducible sound. This sound may provide useful information for diagnosing and addressing the issue. However, without experienced personnel, such as a maintenance crew, for example, in the vehicle at the time that the sound is produced by the vehicle, the issue may be difficult to diagnose. For example, an aircraft mechanic may have to rely on a pilot's memory and documentation to recall an erroneous sound, and may have to painstakingly troubleshoot the vehicle to find the source of the fault.

One or more embodiments may provide a system that uses artificial intelligence to quickly recognize a fault based on an audio signal, which may more effectively detect, identify, diagnose, or resolve a fault of a vehicle. One or more embodiments may provide a system for maintenance personnel to more quickly address, identify, and resolve a fault, such as a mechanical issue, for example, on a vehicle. One or more embodiments may provide a system that improves operator awareness of a severity and cause of an erroneous sound from the vehicle during an operation of the vehicle. One or more embodiments may provide a system that addresses a difficulty in diagnosing a fault that causes a noise that can be heard by an operator of the vehicle, such as in a cockpit of an aircraft, for example, but otherwise does not have an effective method to identify or diagnose the fault.

One or more embodiments may identify, quantify, and locate a fault when the fault occurs. One or more embodiments may provide maintenance personnel with a better indication of a problem with the vehicle or may provide the exact problem, which may reduce vehicle downtime, maintenance time, and cost of operation. One or more embodiments may triangulate the source of the sound in the vehicle and provide an indication of the location of the source of the sound, which maintenance personnel may use to troubleshoot the fault.

One or more embodiments may collect data related to various audio recordings of a vehicle, including both during operation without a fault and during operation with a fault. The collected data may be used to train a machine learning system, such as a neural network, for example, to analyze the collected data, such as by performing sentiment analysis, for example. The system may use artificial intelligence techniques including natural language processing, for example. The trained machine learning system may identify and categorize the collected data related to the audio recordings, and identify a fault correlated with a given audio input.

The system may be implemented onboard the vehicle, and may use existing hardware of the vehicle, such as a pilot headset and/or microphone, or an audio system of a motor vehicle, for example, to monitor sounds of the vehicle during an operation of the vehicle. Upon detecting an audio input correlated with a fault, the system may provide an alert to the operator that identifies the detected fault. Upon detecting an audio input correlated with a fault, the system may record pertinent data, such as the audio signal, a status of the vehicle, and the detected fault, for example, for use by maintenance personnel to resolve the fault of a vehicle.

One or more embodiments may use at least one audio input, such as from a microphone in a headset for a pilot, for example, and may use multiple audio inputs. Using an open audio feed from each microphone, the audio data may be processed by an active noise cancellation filter to reduce constant noise patterns, such as normally operating engine sounds and wind, for example. The system may temporarily store a rolling predetermined amount of audio data, such as audio data for the past two minutes, for example.

When a fault occurs, an operator of the vehicle may log the fault, such as by pressing a button on the vehicle, for example. This log action may trigger the system to store the temporary audio data, so that the temporary audio data is not overwritten, which may be used by maintenance personnel. By algorithmically using the sound of the fault that was picked up in each microphone in the vehicle, the system may triangulate the source of the sound in the vehicle and provide an indication of the location of the source of the sound, which maintenance personnel may use to troubleshoot the fault.

FIG.1depicts an exemplary system infrastructure for a system for using artificial intelligence to analyze an audio signal to detect and identify a fault of a vehicle, according to one or more embodiments. Vehicle100may include a first microphone110, a second microphone120, a user input130, a user output140, a trained machine-learning based model150, vehicle data160, and controller300.

Vehicle100may be a motor vehicle, an aircraft, or a watercraft, for example. First microphone110and second microphone120may be transducers for converting sound waves into an electrical signal. For example, first microphone110and second microphone120may include one or more of a microphone in a headset of a pilot of an aircraft, or a speaker, used as a microphone, in a motor vehicle. First microphone110and second microphone120may each include one or more microphones.

User input130may include an input device for a user to provide an input signal to controller300. User input130may include one or more of a physical button, a virtual button on a touch screen, a microphone for receiving voice commands, or any other device operative to interact with the controller300. User output140may include an output device for a user to receive an output signal from controller300. For example, user output140may include one or more of an indicator light, a speaker, a buzzer, a haptic feedback device, a display, a projector, a printer, or other now known or later developed device for outputting determined information.

Trained machine-learning based model150may be instructions324stored in a memory304of controller300, or may be stored in another form in vehicle100and accessible by controller300. The trained machine-learning based model150that may be useful and effective for the analysis is a neural network, which is a type of supervised machine learning. However, other machine learning techniques and frameworks may be used to perform the methods contemplated by the present disclosure. For example, the systems and methods may be realized using other types of supervised machine learning, such as regression problems or random forest, for example, using unsupervised machine learning such as cluster algorithms or principal component analysis, for example, and/or using reinforcement learning. The trained machine-learning based model150may alternatively or additionally be rule-based.

Vehicle data160may include information providing a status of the vehicle100, such as a speed, location, altitude, or temperature, for example. The vehicle data160may be data from an avionics component of an aircraft, or data from a diagnostic controller in a motor vehicle, for example.

FIG.2depicts a flowchart of a method of using artificial intelligence to analyze an audio signal to detect and identify a fault of a vehicle, according to one or more embodiments. A method200for automatically determining a fault of a vehicle100may include various operations, which may be executed by controller300, for example.

Method200may include receiving one or more audio signals from one or more microphones (e.g. first microphone110and/or second microphone120) of the vehicle100(operation210). One or more of the first microphone110or second microphone120may receive a sound signal from an environment of the vehicle100, such as in a cockpit of an aircraft, for example. The sound signal may include normal sounds of the vehicle100in operation, such as engine noise, wind noise, landing gear operation, air conditioning system noise, auxiliary power unit noise, power transfer unit noise, servo noise, wing flap operation, and pilot communication, for example. The sound signal may include erroneous sounds of the vehicle100in operation, such as excess wind noise, brake pad squealing, engine knocking, and fault buzzers, for example.

Reception of the sound signal may be delayed to second microphone120relative to reception of the sound signal to first microphone110based on a location of first microphone110and second microphone120. One or more of the first microphone110or second microphone120may convert the sound signal to an audio/electrical signal, and send the audio signal to the controller300.

Method200may include extracting diagnostic metadata from the received one or more audio signals (operation220). The extracting the diagnostic metadata from the received one or more audio signals may include using natural language processing. The extracting the diagnostic metadata from the received one or more audio signals may include reducing constant noise patterns in the received one or more audio signals using an active noise cancellation filter. For example, the controller300may use an active noise cancellation filter to reduce or remove signal patterns correlated with the normal sounds of the vehicle100in operation.

The controller may also use vehicle data160to reduce or remove signal patterns correlated with the normal sounds of the vehicle100in operation. For example, a sound from landing gear may be expected during landing procedure, and may be filtered as a normal sound. However, the same sound from landing gear during a cruising procedure in the aircraft may be unexpected, and may not be filtered as a normal sound (i.e. the landing gear noise may be identified as an erroneous sound associated with a fault).

Controller300may further process the noise-filtered signal to extract diagnostic metadata, such as a signal pattern having characteristics such as amplitude, period, frequency, or a phase, for example. The noise-filtered signal may be an analog signal or a digital signal, and the diagnostic metadata may include a timestamp of the signal, one or more characteristics of the signal, or other data to identify the signal.

Method200may include extracting a diagnostic feature from the diagnostic metadata, the extracted diagnostic feature corresponding to a feature of a trained machine-learning based model150for determining a fault based on a learned association between the extracted diagnostic feature and a fault of the vehicle100(operation230). For example, controller300may extract a diagnostic feature such as a frequency of the signal from the diagnostic metadata. Controller300may provide the extracted diagnostic feature as an input to trained machine-learning based model150.

Method200may include automatically determining the fault based on the extracted diagnostic feature, by using the trained machine-learning based model150that was trained based on a first feature extracted from first training metadata regarding previously recorded data and a second feature extracted from metadata regarding a previous fault related to the previously recorded data, based on the learned association between the extracted diagnostic feature and the fault (operation240).

For example, trained machine-learning based model150may be trained to recognize that a signal in a threshold frequency range may be associated with a faulty fan in an air conditioning system of an aircraft. Accordingly, the trained machine-learning based model150may receive the extracted diagnostic feature (e.g. signal frequency) as an input, determine the fault (e.g. faulty fan) based on the received diagnostic feature, and return the fault as an output to controller300.

Method200may include receiving an input signal from a user input130of the vehicle100to create a timestamp for the received one or more audio signals (operation250). For example, an operator of vehicle100may hear an unknown sound during an operation of the vehicle, and may provide an input signal using user input130(e.g. by pressing a button in vehicle100) to controller300to create a timestamp to mark the time that the operator heard the unknown sound.

Method200may include triangulating a location of a source of the received one or more audio signals using the timestamp and the received one or more audio signals (operation260). The one or more microphones may include a first microphone110and a second microphone120. The one or more audio signals may include a first audio signal from the first microphone110and a second audio signal from the second microphone120. The triangulating the location of the source may include using the first audio signal and the second audio signal. For example, controller300may use a known location and geometry of first microphone110and second microphone120, and a difference between the sound signal received by second microphone120and the sound signal received by first microphone110to triangulate the location of the source of the sound.

For example, controller300may compare the sound signal received by second microphone120and the sound signal received by first microphone110, and determine a location of the source of the sound to a position with a forty-degree elevation angle and a ten-degree angle relative to a reference point and central axis in a cockpit of an aircraft. Accordingly, maintenance personnel may use the triangulated location to determine the source of the sound and the fault. Alternatively or additionally, controller300may use the triangulated location along with a known location and geometry of components of vehicle100to determine the source of the sound and the fault.

Method200may include storing, such as in memory304, for example, the received one or more audio signals and the timestamp (operation270). Method200may include storing vehicle data160associated with the received one or more audio signals and the timestamp (operation280). For example, controller300may store the received one or more audio signals and/or the extracted diagnostic metadata along with the timestamp of when the user input was received, thereby providing a general timestamp of when the sound was heard. The vehicle data may include information providing a status of the vehicle, such as a speed, location, altitude, or temperature, for example. Controller300may also store additional data correlated with the sound, such as the triangulated location, extracted diagnostic feature, or the determined fault, for example.

As an example, the vehicle100may be an aircraft, the one or more microphones of the vehicle100may include a headset in a cockpit of the aircraft, and the vehicle data may include avionics data. Method200may include providing an alert to a user output140for an operator of the vehicle100based on the determined fault (operation290). For example, an alert may include one or more of an audio or visual indication. Controller300may provide a textual fault indication to a heads-up display in a motor vehicle, or sound a warning buzzer in an aircraft with a specified tone or period, depending on the determined fault. Method200may include temporarily storing a rolling predetermined amount of the received one or more audio signals. For example, controller300may store two minutes, for example, of received audio data, and may overwrite the stored data on a rolling basis if no fault is detected.

FIG.3depicts an implementation of a controller300that may execute techniques presented herein, according to one or more embodiments. The controller300may include a set of instructions that can be executed to cause the controller300to perform any one or more of the methods or computer based functions disclosed herein.

The controller300may operate as a standalone device or may be connected, e.g., using a network, to other computer systems or peripheral devices.

As illustrated inFIG.3, the controller300may include a processor302, e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both. The processor302may be a component in a variety of systems. For example, the processor302may be part of a standard computer. The processor302may be one or more general processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, servers, networks, digital circuits, analog circuits, combinations thereof, or other now known or later developed devices for analyzing and processing data. The processor302may implement a software program, such as code generated manually (i.e., programmed).

The controller300may include a memory304that can communicate via a bus308. The memory304may be a main memory, a static memory, or a dynamic memory. The memory304may include, but is not limited to computer readable storage media such as various types of volatile and non-volatile storage media, including but not limited to random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tape or disk, optical media and the like. In one implementation, the memory304includes a cache or random-access memory for the processor302. In alternative implementations, the memory304is separate from the processor302, such as a cache memory of a processor, the system memory, or other memory. The memory304may be an external storage device or database for storing data. Examples include a hard drive, compact disc (“CD”), digital video disc (“DVD”), memory card, memory stick, floppy disc, universal serial bus (“USB”) memory device, or any other device operative to store data. The memory304is operable to store instructions executable by the processor302. The functions, acts or tasks illustrated in the figures or described herein may be performed by the processor302executing the instructions stored in the memory304. The functions, acts or tasks are independent of the particular type of instructions set, storage media, processor or processing strategy and may be performed by software, hardware, integrated circuits, firm-ware, micro-code and the like, operating alone or in combination. Likewise, processing strategies may include multiprocessing, multitasking, parallel processing and the like.

As shown, the controller300may further include a display310, such as a liquid crystal display (LCD), an organic light emitting diode (OLED), a flat panel display, a solid-state display, a cathode ray tube (CRT), a projector, a printer or other now known or later developed display device for outputting determined information. The display310may act as an interface for the user to see the functioning of the processor302, or specifically as an interface with the software stored in the memory304or in the drive unit306.

Additionally or alternatively, the controller300may include an input device312configured to allow a user to interact with any of the components of controller300.

The input device312may be a number pad, a keyboard, or a cursor control device, such as a mouse, or a joystick, touch screen display, remote control, or any other device operative to interact with the controller300.

The controller300may also or alternatively include drive unit306implemented as a disk or optical drive. The drive unit306may include a computer-readable medium322in which one or more sets of instructions324, e.g. software, can be embedded. Further, the instructions324may embody one or more of the methods or logic as described herein. The instructions324may reside completely or partially within the memory304and/or within the processor302during execution by the controller300. The memory304and the processor302also may include computer-readable media as discussed above.

In some systems, a computer-readable medium322includes instructions324or receives and executes instructions324responsive to a propagated signal so that a device connected to a network370can communicate voice, video, audio, images, or any other data over the network370. Further, the instructions324may be transmitted or received over the network370via a communication port or interface320, and/or using a bus308. The communication port or interface320may be a part of the processor302or may be a separate component. The communication port or interface320may be created in software or may be a physical connection in hardware. The communication port or interface320may be configured to connect with a network370, external media, the display310, or any other components in controller300, or combinations thereof. The connection with the network370may be a physical connection, such as a wired Ethernet connection or may be established wirelessly as discussed below. Likewise, the additional connections with other components of the controller300may be physical connections or may be established wirelessly. The network370may alternatively be directly connected to a bus308.

While the computer-readable medium322is shown to be a single medium, the term “computer-readable medium” may include a single medium or multiple media, such as a centralized or distributed database, and/or associated caches and servers that store one or more sets of instructions. The term “computer-readable medium” may also include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by a processor or that cause a computer system to perform any one or more of the methods or operations disclosed herein. The computer-readable medium322may be non-transitory, and may be tangible.

The controller300may be connected to a network370. The network370may define one or more networks including wired or wireless networks. The wireless network may be a cellular telephone network, an802.11,802.16,802.20, or WiMAX network. Further, such networks may include a public network, such as the Internet, a private network, such as an intranet, or combinations thereof, and may utilize a variety of networking protocols now available or later developed including, but not limited to TCP/IP based networking protocols. The network370may include wide area networks (WAN), such as the Internet, local area networks (LAN), campus area networks, metropolitan area networks, a direct connection such as through a Universal Serial Bus (USB) port, or any other networks that may allow for data communication. The network370may be configured to couple one computing device to another computing device to enable communication of data between the devices. The network370may generally be enabled to employ any form of machine-readable media for communicating information from one device to another. The network370may include communication methods by which information may travel between computing devices. The network370may be divided into sub-networks. The sub-networks may allow access to all of the other components connected thereto or the sub-networks may restrict access between the components. The network370may be regarded as a public or private network connection and may include, for example, a virtual private network or an encryption or other security mechanism employed over the public Internet, or the like.

In accordance with various implementations of the present disclosure, the methods described herein may be implemented by software programs executable by a computer system. Further, in an exemplary, non-limited implementation, implementations can include distributed processing, component/object distributed processing, and parallel processing. Alternatively, virtual computer system processing can be constructed to implement one or more of the methods or functionality as described herein.

It will be understood that the steps of methods discussed are performed in one embodiment by an appropriate processor (or processors) of a processing (i.e., computer) system executing instructions (computer-readable code) stored in storage. It will also be understood that the disclosure is not limited to any particular implementation or programming technique and that the disclosure may be implemented using any appropriate techniques for implementing the functionality described herein. The disclosure is not limited to any particular programming language or operating system.

One or more embodiments may provide a system that uses artificial intelligence to quickly recognize a fault based on an audio signal, which may more effectively detect, identify, diagnose, or resolve a fault of a vehicle. One or more embodiments may provide a system for maintenance personnel to more quickly address, identify, and resolve a fault, such as a mechanical issue, for example, on a vehicle. One or more embodiments may provide a system that improves operator awareness of a severity and cause of an erroneous sound from the vehicle during an operation of the vehicle. One or more embodiments may provide a system that addresses a difficulty in diagnosing a fault that causes a noise that can be heard by an operator of the vehicle, such as in a cockpit of an aircraft, for example, but otherwise does not have an effective method to identify or diagnose the fault. One or more embodiments may identify, quantify, and locate a fault when the fault occurs. One or more embodiments may provide maintenance personnel with a better indication of a problem with the vehicle or may provide the exact problem, which may reduce vehicle downtime, maintenance time, and cost of operation. One or more embodiments may triangulate the source of the sound in the vehicle and provide an indication of the location of the source of the sound, which maintenance personnel may use to troubleshoot the fault.