Patent Description:
Aircraft require electrical power to operate many parts of the aircraft system, including on-board flight control systems, lighting, air conditioning etc. The current and future generations of aircraft use more and more electrical control in place of convention hydraulic, pneumatic etc. control. Such more electric aircraft (MEA) have advantages in terms of the size and weight of the controls and power systems as well as in terms of maintenance and reliability.

Most current large commercial aircraft use electricity, on-board, in the form of an alternating (AC) fixed frequency and/or variable frequency network. Steps have been made to move from <NUM> V AC to <NUM> V AC and more recent developments have allowed power supplies to supply high voltage direct current (HVDC) e.g. <NUM> V DC or more, providing improvements in terms of additional functionality, power supply simplification, weight savings and, thus, fuel efficiency. These HVDC networks can be particularly susceptible to faults. Fault detection within these HVDC networks is of particular interest due to the potential for damage to the sensitive components aboard aircraft. There exists a need for quickly determining faults in these networks. Fault detection systems are known from e.g. <CIT>, <CIT> and <CIT>.

A first aspect of the present invention is directed to a system as defined by claim <NUM>.

Embodiments of the system may include that the AC test signal to determine a fault of the power channel based on comparing the predefined test signal pattern with a predefined nominal probe signal pattern corresponding to a specific network configuration comprises transforming, using a continuous wavelet transform, the continuous power signal to a first scalogram, generating, using a machine learning model, a first feature vector comprising a plurality of features extracted from the first scalogram, and plotting the first feature vector in a feature space to determine the fault in the power channel.

Embodiments of the system may include that the feature space comprises a first set of feature vectors indicating a normal operation of the power channel based on the predefined test signal pattern.

Embodiments of the system may include that determining the fault in the power channel comprises determining a distance value between the first feature vector and the first set of feature vectors indicating the normal operation of the power channel determining the fault in the power channel based on the distance value exceeding a pre-defined threshold value.

Embodiments of the system may include that the feature space comprise a second set of feature vectors indicating a first fault type of the power channel.

Embodiments of the system may include that the controller is further configured to determine a second distance value between the first feature vector and the second set of feature vectors indicating the first fault type of the power channel, and determining the first fault type based on the second distance value being within a second pre-defined threshold value.

Embodiments of the system may include that the feature space is generated using labeled training data for the predefined test signal pattern.

Embodiments of the system may include that the feature space is generated based on unlabeled training data comprising historical data for the continuous power signal and the predefined test signal pattern.

Embodiments of the system may include a second load coupled to the power channel and a second receiving sensor coupled to the power channel between the transmitting sensor and the second load, wherein the controller is in electronic communication with the second receiving sensor.

Embodiments of the system may include that the controller is further configured to operate the second receiving sensor to sense, from the power channel, the continuous power signal and analyze the continuous power signal to determine the fault in the power channel based on comparing the continuous power signal to the predefined signal pattern.

Embodiments of the system may include that the first power supply comprises a direct current (DC) power supply, wherein the DC power supply comprises an AC generator and rectification circuit.

Embodiments of the system may include that the load comprises an AC motor in an aircraft.

Another aspect of the present invention is directed to a method as defined by claim <NUM>.

Embodiments of the method may include that the feature space comprises a first set of feature vectors indicating a normal operation of the power channel based on the predefined test signal pattern.

Embodiments of the method may include that determining the fault in the power channel comprises determining a distance value between the first feature vector and the first set of feature vectors indicating the normal operation of the power channel determining the fault in the power channel based on the distance value exceeding a pre-defined threshold value.

Embodiments of the method may include that the feature space comprise a second set of feature vectors indicating a first fault type of the power channel.

Embodiments of the method may include determining a second distance value between the first feature vector and the second set of feature vectors indicating the first fault type of the power channel, and determining the first fault type based on the second distance value being within a second pre-defined threshold value.

Embodiments of the method may include that the feature space is generated using labeled training data for the predefined test signal pattern.

Embodiments of the method may include that the feature space is generated based on unlabeled training data comprising historical data for the continuous power signal and the predefined test signal pattern.

The diagrams depicted herein are illustrative. There can be many variations to the diagram or the operations described therein without departing from the spirit of the invention. For instance, the actions can be performed in a differing order or actions can be added, deleted or modified. Also, the term "coupled" and variations thereof describes having a communications path between two elements and does not imply a direct connection between the elements with no intervening elements/connections between them. All of these variations are considered a part of the specification.

<FIG> illustrates an example of a commercial aircraft <NUM> having aircraft engines <NUM> the engine or the aircraft has the invention? that may embody aspects of the teachings of this disclosure. The aircraft <NUM> includes two wings <NUM> that each include one or more slats <NUM> and one or more flaps <NUM>. The aircraft further includes ailerons <NUM>, spoilers <NUM>, horizontal stabilizer trim tabs <NUM>, rudder <NUM> and horizontal stabilizer <NUM>. The term "control surface" used herein includes but is not limited to either a slat or a flap or any of the above described. The aircraft <NUM> also includes one or more DC distribution networks <NUM> (described in greater detail in <FIG>) which includes high frequency sensors for determine faults and for health management.

Turning now to an overview of technologies that are more specifically relevant to aspects of the disclosure, electrical component power inside aircrafts is projected to increate to over <NUM> megawatts within the next decade. This increase will require high voltage direct current (HVDC) distribution networks for both propulsion and energy storage systems within the aircraft. Next generation HVDC distribution networks are expected to operate between <NUM> to <NUM> kV to optimally balance both transmission power and weight requirements of the distribution network elements. Along with these new HVDC distribution network in an aircraft environments comes a higher probability of electrical faults. Some particularly difficult to detect fault types include partial discharge faults, series arc faults, and parallel arc faults. The partial discharge faults can cause damage to insulation over time due to the high altitude and lower pressure aircraft environments. The series arc faults can be caused by corrosion to series connectors and/or loose connections in series with electrical loads. Further, series arc faults can increase power loss and cause additional heating to occur over time. The parallel arc faults occur when there is electrical arcing between two line conductors or between a line conductor and ground. These parallel arc faults can cause immediate power loss.

Series arc-faults and partial discharge related degradation are often difficult to detect in an HVDC distribution network. To address this difficulty in detecting these faults, one or more embodiments provide for the inclusion of high frequency (HF) sensors to be attached to the power channels in the HVDC distribution network of an aircraft. These HF sensors include radio frequency (RF) transmitters/receivers (transceivers) and impedance matching networks. In one or more embodiments, the HF sensors also include a controller for controlling any of the components of the sensor and/or for signal processing. In one or more embodiments, a controller can be separate from the HF sensors and in electronic communication with the HF sensors. In one or more embodiments, the HF sensors are configured to inject high frequency alternating current (AC) voltage test signals in a specific pattern into the DC distribution network. Additional HF sensors can be configured to receive these injected test signals for signal processing and analysis. The data captured by the HF sensors is analyzed using wavelet transformation. Further, with continuous monitoring and time series classification using continuous time wavelet transformations, a profile of the DC distribution network can be created during normal operation. Or, in some embodiments, a normal operation profile can be created for a DC distribution network offline.

In one or more embodiments, the normal operation profile can be utilized by the HF sensors to determine faults in the DC distribution network. As an HF sensor injects the test AC signal into the distribution networks, the receiving HF sensors can receive this test AC signal and compare the test signal to the normal operation profile to determine an existence of a fault and further determine a type of fault in the DC distribution network. The HF sensors, including control circuitry (controllers), can implement machine learning models and/or neural network technology to compare data/signals from the HF sensor sending the test AC signal with a normal operation profile of the DC distribution network. Further, the machine learning models and/or neural networks can implement clustering algorithms for the data/signals to determine a type of fault in the DC distribution network. In other embodiments, the data/signal processing described above can be performed local to the HF sensors or the data can be transferred to a higher level control circuitry for signal processing as described above.

<FIG> depicts a block diagram DC distribution network including HF sensors according to one or more embodiments. The DC distribution network <NUM> can, for example, be implemented in an aircraft environment similar to the aircraft <NUM> described in <FIG>. Further, the DC distribution network <NUM> includes a DC power supply <NUM> which supplies DC power in the DC distribution network <NUM> to one or more motors <NUM>. While only one DC power supply <NUM> and two motors <NUM> are shown in the illustrated example, any number of DC power supplies and motors can be utilized herein. In one or more embodiments, the DC power supply <NUM> includes an AC generator, a protection device, and an AC to DC power converter (rectifier) (not shown). The AC generator can be any type of generator such as a generator that receives mechanical input from an engine of an aircraft, for example. The protection device can be any type of circuit breaker attached between the AC generator and the rectifier. The rectifier provides high voltage rectified DC power to the power channel <NUM> (sometimes referred to as a DC bus) of the DC distribution network <NUM>. Attached to this power channel <NUM> are motors <NUM> coupled to a power converter <NUM>. The power converter <NUM> can be a DC to AC power converter (inverter) that converts the DC power supplied to the power channel <NUM> to AC power to drive the AC motors <NUM>. A protection device <NUM> is coupled to the power channel <NUM> before the power converter <NUM> to provide protection to the AC motors <NUM>. The DC power supply <NUM> can be a high voltage DC source. Also, the power converter <NUM> and motors <NUM> can be a high voltage DC load on the network <NUM>. The CB <NUM> is a high voltage DC protection device.

In one or more embodiments, the DC distribution network <NUM> includes high frequency (HF) sensors <NUM>, <NUM>. The HF sensors <NUM>, <NUM> are coupled to the power channel <NUM> at different locations in the DC distribution network <NUM>. A transmitting HF sensor <NUM> is located at the output of the DC power supply <NUM> and receiving HF sensors <NUM> are located at the motors <NUM> in the distribution network <NUM>. The HF sensors <NUM>, <NUM> each include components configured to both transmit and receive signal data (i.e., sensed voltages/current) from the power channel <NUM>. The HF sensors <NUM>, <NUM> also include control circuitry and at least one memory and/or storage. Further, the HF sensors <NUM>, <NUM> include components that can be either passive (combinations of L,C,R) or active operational amplifier components to match the input impedance of the sensor nodes to the impedance of the high voltage DC distribution network. In one or more embodiments, the control circuitry in the HF sensors <NUM>, <NUM> can communicate with a controller <NUM> in electronical communication with the HF sensors <NUM>, <NUM> through a communication channel <NUM>. Or, in some embodiments, the HF sensors <NUM>, <NUM> can communicate with each other through communication channel <NUM>.

In one or more embodiments, the HF sensors <NUM>, <NUM> coupled to the power channel <NUM> can be utilized to test the DC distribution network <NUM> for potential faults as described above. The transmitting HF sensor <NUM>, attached near the output of the DC power supply <NUM>, can inject a high frequency AC voltage test signal in a specified pattern into the power channel <NUM>. In one or more embodiments, the DC power supply is a high voltage DC power supply. The high voltage DC distribution network acts an RF communication channel in addition to transmitting power from DC sources to loads. A set of receiving HF sensors <NUM> can be placed at strategic locations in this power network <NUM> and are configured to receive the high frequency AC test signal from the power channel <NUM>. The receiving HF sensors <NUM> which include controllers/control circuitry can filter the test signal pattern from the ambient electromagnetic noise generated by sources and loads present in the DC distribution network <NUM> and process this test signal pattern. The receiving HF sensors <NUM> can have multiple test signal patterns that indicate normal operation of the distribution network for different operating conditions and different network configurations. The HF sensor <NUM> control circuitry can process the received test signal patterns and compare them to the one or more test signals patterns stored in memory indicative of normal operations to determine whether there is a fault or other issue in the distribution network <NUM>. In one or more embodiments, the controller <NUM> can perform this comparison step with the HF sensors <NUM> transmitting the signal pattern data to the controller <NUM> for processing and comparison.

In one or more embodiments, the one or more test signal patterns that indicate normal operation of the DC distribution network <NUM> can be created offline using initial configuration conditions of the network <NUM>. These test signal patterns for normal operation can be created using circuit simulations, circuit testing software, actual component testing, and/or any other test signal pattern generation techniques. The one or more test signal patterns can be stored as a normal operation profile for the network <NUM> in the HF sensors <NUM>, <NUM>. During operation of the distribution network <NUM>, the transmitting HF sensor <NUM> provides the high frequency AC test signal to the power channel <NUM> which is received by each receiving HF sensors <NUM>. The received test signal is compared to the normal operation profile for determination of detected faults. As each motor <NUM> includes a receiving HF sensor <NUM>, the location of any detected faults can be further discerned based on a first receiving HF sensor determining a fault and a second receiving HF sensor not determining a fault. In this scenario, the fault would be located on a branching of the power channel <NUM> that is connected to the HF sensor determining the fault.

In one or more embodiments, the determination of a fault in the DC distribution network <NUM> can be performed utilizing one or more machine learning models and/or neural networks. In one or more embodiments, classification and/or clustering can be utilized for comparing the AC test signal patterns to normal operation patterns when determining a fault in the network <NUM>. Classification and clustering can be suitable for pattern recognition problems such as the fault detection using AC test signal patterns described herein. The AC test signal patterns injected into the DC power channel <NUM> are received by the receiving HF sensors <NUM> as a time domain signal. In one or more embodiments, the HF sensors <NUM> control circuitry can analyze the AC test signals using continuous wavelet transformations. <FIG> depicts two graphs illustrating a continuous wavelet transformation from a time domain graph to a scalogram according to one or more embodiments. The first graph <NUM> depicts the time domain signal being sensed/detected by the receiving HF sensor <NUM>. The HF sensors <NUM> control circuitry and/or controller <NUM> can transform the time domain signal into a scalogram as depicted in the second graph <NUM> using continuous wavelet transforms. A continuous wavelet transform decomposes a signal using wavelets, which are generally highly localized in time. The continuous wavelet transform may provide a higher resolution relative to discrete transforms, thus providing the ability to garner more information from signals than typical frequency transforms such as Fourier transforms (or any other spectral techniques) or discrete wavelet transforms. Continuous wavelet transforms allow for the use of a range of wavelets with scales spanning the scales of interest of a signal such that small scale signal components correlate well with the smaller scale wavelets and thus manifest at high energies at smaller scales in the transform. Likewise, large scale signal components correlate well with the larger scale wavelets and thus manifest at high energies at larger scales in the transform. Thus, components at different scales may be separated and extracted in the wavelet transform domain. Moreover, the use of a continuous range of wavelets in scale and time position allows for a higher resolution transform than is possible relative to discrete techniques.

In one or more embodiments, the scalogram <NUM> can be created from the received signal data sensed/detected by the receiving HF sensors <NUM> and compared to a scalogram of a normal operation signal pattern using image recognition, classification, and/or clustering techniques (referred to collectively as "machine learning techniques"). In one or more embodiments, the scalogram is not the sole processing technique, other signal processing techniques for one-dimensional time varying functions can also be used. The advantage of CWT is that the resulting two dimensional matrix can be easily processed by optimized neural network techniques. The HF sensor <NUM> control circuitry and/or controller <NUM> can create feature vectors of the scalogram, for example, which include multiple feature values extracted from characteristics of the scalogram. These feature vectors can be plotted in a multidimensional feature space and compared to other plotted feature vectors in the multidimensional feature space to determine that a fault exists. The multidimensional feature space can be pre-populated with feature vectors that have been labeled with labels such as normal operation, partial discharge fault, series arc fault, and parallel arc fault, for example. The proximity of the feature vector taking from a scalogram for an AC test signal can dictate the overall health of the distribution network <NUM>. For example, if the feature vectors from test signals are within a threshold proximity in the feature space to the feature vectors for normal operation, then the distribution network <NUM> can be performing as anticipated. However, if the test signal feature vectors are more proximate to faults or outside a threshold for normal operation, then this would indicate a potential issue with the network <NUM>. As mentioned above, the training of the feature space can be done offline using labeled training data where predefined signal patterns are injected in a DC distribution network with anticipated scalograms being generated and feature vectors are labeled as normal operation or as different fault types depending on the training and labels. For example, for a series arc fault, a simulation could dictate what the predefined test signal would look like at the receiving sensor <NUM> and a resultant signal transformed to a scalogram could be used for training the machine learning model (i.e., feature space). When in operation, the feature space includes sets of feature vectors pre-plotted indicating the normal operation and other sets of feature vectors indicating faults and fault types. When the real-time signal data is being sensed by the sensors <NUM>, features are extracted from the data to be plotted and compared to the labeled vectors to make determinations as to faults and fault types or if the network is operating in normal operations.

In one or more embodiments, the feature space (machine learning model) can be trained using unlabeled training data. This unlabeled training data can include historical operation data of the network <NUM> which is received by the HF sensors <NUM> and clustered and plotted in the feature space. The historical operation data would include the pre-defined test signals being injected into the power channel <NUM>.

The control circuitry and/or controller <NUM> or any of the hardware referenced in the system <NUM> can be implemented by executable instructions and/or circuitry such as a processing circuit and memory. The processing circuit can be embodied in any type of central processing unit (CPU), including a microprocessor, a digital signal processor (DSP), a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or the like. Also, in embodiments, the memory may include random access memory (RAM), read only memory (ROM), or other electronic, optical, magnetic, or any other computer readable medium onto which is stored data and algorithms as executable instructions in a non-transitory form.

In one or more embodiments, the control circuitry and/or controller <NUM> can execute machine readable instructions to implement so-called engines/classifiers. In one or more embodiments of the invention, the features of the various engines/classifiers described herein can be implemented as described above, or can be implemented on a neural network (not shown). In one or more embodiments, the features of the engines/classifiers can be implemented by configuring and arranging the control circuitry and/or controller <NUM> to execute machine learning (ML) algorithms. In general, ML algorithms, in effect, extract features from received data (e.g., the test signal patterns, etc.) in order to "classify" the received data. Examples of suitable classifiers include but are not limited to neural networks (described in greater detail below), support vector machines (SVMs), logistic regression, decision trees, hidden Markov Models (HMMs), etc. The end result of the classifier's operations, i.e., the "classification," is to predict a class for the data. The ML algorithms apply machine learning techniques to the received data in order to, over time, create/train/update a unique "model. " The learning or training performed by the engines/classifiers can be supervised, unsupervised, or a hybrid that includes aspects of supervised and unsupervised learning. Supervised learning is when training data is already available and classified/labeled. Unsupervised learning is when training data is not classified/labeled so must be developed through iterations of the classifier. Unsupervised learning can utilize additional learning/training methods including, for example, clustering, anomaly detection, neural networks, deep learning, and the like. In one or more embodiments where the engines/classifiers are implemented as neural networks, a resistive switching device (RSD) can be used as a connection (synapse) between a pre-neuron and a post-neuron, thus representing the connection weight in the form of device resistance. Neuromorphic systems are interconnected processor elements that act as simulated "neurons" and exchange "messages" between each other in the form of electronic signals. Similar to the so-called "plasticity" of synaptic neurotransmitter connections that carry messages between biological neurons, the connections in neuromorphic systems such as neural networks carry electronic messages between simulated neurons, which are provided with numeric weights that correspond to the strength or weakness of a given connection. The weights can be adjusted and tuned based on experience, making neuromorphic systems adaptive to inputs and capable of learning. For example, a neuromorphic/neural network for handwriting recognition is defined by a set of input neurons, which can be activated by the pixels of an input image. After being weighted and transformed by a function determined by the network's designer, the activations of these input neurons are then passed to other downstream neurons, which are often referred to as "hidden" neurons. This process is repeated until an output neuron is activated. Thus, the activated output neuron determines (or "learns") which character was read. Multiple pre-neurons and post-neurons can be connected through an array of RSD, which naturally expresses a fully-connected neural network.

<FIG> depicts a block diagram of an exemplary HF sensor according to one or more embodiments. The HF sensor <NUM> includes a digital controller <NUM> in electronic communication with a modem <NUM>. The controller <NUM> can provide a test signal for the high voltage DC distribution network <NUM>. The signal is provided through one or more transmit filters <NUM> and an analog impedance match network <NUM> to the high voltage DC distribution network <NUM>. Any signals from the HVDC network <NUM> can be received by the controller <NUM> through the analog impedance match <NUM> and one or more receive filters <NUM>.

<FIG> depicts a flow diagram of a method for health monitoring and fault detection in power distribution networks according to one or more embodiments. The method <NUM> includes providing a first power supply coupled to a power channel, a load coupled to the power channel, a transmitting sensor coupled to the power channel between the first power supply and the load, and a receiving sensor coupled to the power channel between the transmitting sensor and the load, as shown in block <NUM>. The transmitting sensors and receiving sensors include transceivers and control circuitry. At block <NUM>, the method <NUM> includes operating, by a controller, the transmitting sensor to provide an alternating current (AC) test signal to the power channel, wherein the AC test signal comprises a predefined test signal pattern. Then, at block <NUM>, the method <NUM> includes operating, by the controller, the receiving sensor to sense, from the power channel, a continuous power signal including the AC test signal. And at block <NUM>, the method <NUM> includes analyzing, by the controller, the AC test signal to determine a fault of the power channel based on comparing the predefined test signal pattern with a predefined nominal probe signal pattern corresponding to a specific network configuration.

Claim 1:
A system comprising:
a first power supply (<NUM>) coupled to a power channel (<NUM>);
a load (<NUM>) coupled to the power channel;
a transmitting sensor (<NUM>) coupled to the power channel between the first power supply and the load;
a receiving sensor (<NUM>) coupled to the power channel between the transmitting sensor and the load;
a controller (<NUM>) in electronic communication with the transmitting sensor and the receiving sensor, the controller configured to:
operate the transmitting sensor to provide an alternating current (AC) test signal to the power channel, wherein the AC test signal comprises a predefined test signal pattern;
operate the receiving sensor to sense, from the power channel, a continuous power signal including the AC test signal;
analyze the AC test signal to determine a fault of the power channel based on comparing the predefined test signal pattern with a predefined nominal probe signal pattern corresponding to a specific network configuration; characterized in that
analyzing the AC test signal to determine the fault in the power channel based on comparing the predefined test signal pattern with a predefined nominal probe signal pattern corresponding to a specific network configuration comprises:
transforming, using a continuous wavelet transform, the continuous power signal pattern to a first scalogram;
generating, using a machine learning model, a first feature vector comprising a plurality of features extracted from the first scalogram;
plotting the first feature vector in a feature space to determine the fault in the power channel.