151 results about "Fault class" patented technology

The invention discloses a ship intelligent fault diagnosis method and system based on open label space identification. The method comprises the following steps: obtaining a multi-source sensor time sequence signal of a ship system and constructing a training sample set; carrying out feature extraction on the training sample set by utilizing a deep neural network model, and strengthening the clustering characteristics of the features by adopting a center loss function in the training process; training a classifier on the basis of feature extraction and introducing an open set loss function to form a comprehensive objective function; extracting features from a to-be-diagnosed sample, embedding the features, calculating the distance between the to-be-diagnosed sample and a known fault category feature center, and judging an unknown fault through comparison between the minimum distance and a preset threshold value; and outputting a known fault category or triggering an unknown fault alarm according to a judgment result, and dynamically expanding and updating a model knowledge base based on accumulated unknown fault samples. The method can break through the limitation of the traditional closed set hypothesis, effectively identifies the unknown fault type, and achieves the self-adaptive learning and continuous optimization of a ship fault diagnosis system.

The invention relates to the technical field of deep transfer learning, and discloses a semi-supervised multi-source-domain generalization fault diagnosis method and system based on mutual information, and the method comprises the steps: obtaining fault vibration signal data of a manual fault test bed bearing, and obtaining labeled source domain data, unlabeled source domain data and unknown target domain data; the method comprises the following steps: extracting deep features of label source domain data and label-free source domain data, classifying the deep features, and generating a preliminary false label and confidence of the label-free source domain data; screening the preliminary pseudo tags based on an adaptive threshold strategy, determining high-confidence pseudo tags, and correcting low-confidence pseudo tags to obtain an updated source domain feature set; and training a double-branch classifier by using the updated source domain feature set, and predicting a fault category label of unknown target domain data. The data of the target domain does not need to participate in training, and the unknown target domain can be diagnosed only by using the source domain data, so that the accuracy of fault diagnosis is improved.

The invention provides a single-source-domain generalization intelligent diagnosis method based on targeted data enhancement, and the method comprises the steps: collecting a state monitoring vibration signal of a key part of industrial equipment under different working conditions, and carrying out the data preprocessing, and the preprocessing comprises signal interception and normalization processing; dividing the state monitoring vibration signals into a source domain sample set and a target domain sample set; constructing a basic fault diagnosis model, wherein the model comprises a targeted enhancement chain, a distributed mixing layer, a feature extractor and a fault classifier; inputting the source domain sample set into the basic fault diagnosis model for model training to obtain a target fault diagnosis model; and inputting the target domain sample set into a target fault diagnosis model, and outputting a fault category of the sample. According to the method, the fault recognition accuracy and robustness under the unknown target working condition are remarkably improved.

This application discloses a data-driven energy dispatch fault diagnosis method, device, and medium, mainly relating to the field of fault diagnosis technology. It addresses the problems of existing solutions neglecting the multimodal distribution characteristics of energy data, failing to effectively capture the complex coupling relationships between the source-grid-load-storage links, lacking awareness of dispatch strategies, and being sensitive to noise interference. The method includes: obtaining a graph convolutional hidden representation matrix and then calculating a sparse dictionary matrix; optimizing the graph convolutional sparse coding objective function, solving for the sparse coding coefficients, and outputting a feature mining data matrix; calculating a dispatch strategy matching degree matrix based on the feature mining data matrix, using introduced dispatch plan data and real-time operational constraints; fusing the operational status classification vector and the dispatch strategy matching degree matrix to obtain predicted fault categories; and iteratively training a diagnostic model based on the predicted fault categories and labeled fault categories until a well-trained diagnostic model is obtained.

The embodiment of the specification discloses a photovoltaic module fault detection method, device, computer equipment and storage medium, through target detection on a photovoltaic module image, a preliminary fault detection result is obtained, and in the case that a preliminary fault category included in the preliminary fault detection result belongs to a first easy misjudgment category, image classification is performed on a fault area image included in the photovoltaic module image, a fault classification result corresponding to the fault area image is obtained, and the preliminary fault detection result and the fault classification result are further compared, so that the target fault category is determined based on the comparison result, in this way, through the cooperation of target detection and classification detection, when a fault category misjudged due to the similar image features of the fault area image is identified, the accuracy of fault category identification is improved.

This invention relates to the field of fault diagnosis of mechanical equipment components, and discloses a method, system, medium, and device for fault diagnosis of wet friction components based on MLLM-PINN. The method includes: acquiring three types of basic data (temperature, vibration, and text) of the wet friction component, preprocessing them, and inputting them into an MLLM multimodal feature fusion model to form a high-dimensional fusion feature containing visual, dynamic, and semantic information; constructing a PINN physical constraint model consisting of five fully connected hidden layers using PINN, inputting the high-dimensional fusion feature into the model to output fault category, physical parameter deviation, and damage degree; using a hybrid loss function to perform cross-model collaborative optimization of the MLLM multimodal feature fusion model and the PINN physical constraint model, and optimizing the model parameters to obtain the trained and optimized MLLM multimodal feature fusion model and PINN physical constraint model; and generating a structured diagnostic report for the wet friction component based on the physical prediction results of PINN and the semantic reasoning capability of a large language model.

The application discloses a kind of power electronic system fault diagnosis methods combined with resampling and integrated learning, belong to power electronic equipment fault diagnosis technical field, the method includes real-time sampling electric power electronic system in the current data of one-half fundamental period, current data is normalized, and normalized data is obtained;According to the normalized data, based on fast fourier transform algorithm, the frequency domain characteristics of normalized data are obtained;According to the frequency domain characteristics, the feature vector of normalized data is obtained by feature extraction selector;According to the feature vector of normalized data, the fault class label is obtained using integrated classification model, and the electric power electronic system fault diagnosis is completed.The application solves the technical problems that the original data sample is unbalanced in the existing fault diagnosis method, which leads to inaccurate diagnosis and even wrong judgment of the existing machine learning model, effectively realizes the diagnosis of various fault types of sensors and power devices of electric power electronic system.

The application discloses a composite network fault classification model based on data intelligence, a training method and a fault classification method, belongs to the field of network fault classification, is used for solving the network fault classification problem, and has the technical points that the embedding vectors corresponding to the same fault category of the fault categories belonging to the minority class are subjected to oversampling operation by a first CVAE model to obtain the synthetic embedding vectors of the fault categories; the embedding vectors corresponding to the same fault category of the fault categories belonging to the majority class are subjected to downsampling operation by the first CVAE model to obtain the representative embedding vectors of the fault categories; a fifth training set is constructed according to the synthetic embedding vectors corresponding to the fault categories belonging to the minority class and the representative embedding vectors corresponding to the fault categories belonging to the majority class; a third BERT model and a classifier adapted to the third BERT model are fine-tuned by using the fifth training set to obtain a fourth BERT model; and the fourth BERT model and the classifier adapted to the fourth BERT model are the network fault classification model.

The present application relates to the technical field of fault prediction and computer big data processing, and particularly relates to a generalized zero-shot composite fault diagnosis method, device and system based on counterfactual reasoning. The generalized zero-shot composite fault diagnosis method based on counterfactual reasoning proposed by the present application constructs a two-stage generalized zero-shot composite fault diagnosis model based on counterfactual reasoning. The model firstly points out the internal causal components of fault data from the perspective of causality theory, and then constructs a structural causal model to describe the decoupling and generation of fault features under the guidance of counterfactual reasoning. On this basis, the model improves the generative model by strengthening the discriminator in the first stage to realize the binary classification of single fault and composite fault. In the second stage, the supervised training of the classifier is used to predict the single fault category, and a traditional zero-shot learning method is designed to classify the composite fault. The present application greatly improves the diagnosis accuracy of the model and solves the problem of model diagnosis deviation on visible and invisible classes.

The invention belongs to the technical field of new energy automatic optimization, and discloses a fault diagnosis and optimization method based on a rough set and a random forest, and related equipment. The fault diagnosis and optimization method based on the rough set and the random forest comprises the following steps: inputting multi-source heterogeneous operation data into a constructed fault diagnosis model, and outputting early warning information, the early warning information comprising a fault category; wherein the fault diagnosis model training method comprises the steps of performing knowledge reduction on a multi-dimensional data set by adopting a rough set algorithm, inputting an equipment operation sample set after knowledge reduction into a random forest model, performing multi-target optimization analysis through a big data platform based on early warning information and real-time operation data, and generating decision support scheme data; according to the method, a safe and executable operation and maintenance strategy and an operation instruction can be generated in real time by utilizing a multi-objective optimization technology, and the closed-loop technical bottleneck from data fusion, intelligent diagnosis to dynamic optimization is effectively broken through.

Systems, methods, and techniques that facilitate application fingerprint generation are provided. One or more embodiments described herein can comprise a computer-implemented method comprising determining, by a device operatively coupled to a processor, fault proneness of one or more microservices regarding one or more fault categories, generating, by the device, an ordered seed set of the one or more microservices based on the determined fault proneness, generating, by the device, an augmented ordered seed set of the one or more microservices based on topological relationships of the one or more microservices and based on the ordered seed set, and building, by the device, a set of one or more patterns of resource constraint faults and a set of one or more patterns of entity experience faults based on the augmented ordered seed set of the one or more microservices.

The application relates to the technical field of artificial intelligence, in particular to a training method and device for a fault diagnosis model of a variable-configuration aircraft. The method comprises the following steps: obtaining an initial data set, wherein the initial data set comprises initial flight sequence data, a fault type and initial configuration parameters; constructing a training data set according to the initial data set, wherein the fault type and an initial symbol sequence are labeled as labeled information of the training data set; inputting the training data set into an initial fault diagnosis model to obtain predicted information output by the initial fault diagnosis model; determining a target loss value according to the predicted information and the labeled information; and updating model parameters of the initial fault diagnosis model based on the target loss value to obtain a target fault diagnosis model. The training method for the fault diagnosis model of the variable-configuration aircraft can realize the collaborative optimization of the feature extraction capability of the model on the flight sequence data, the classification capability of the model on the fault categories and the generation capability of the model on the diagnosis text.

The invention discloses a two-stage adversarial migration fault diagnosis method based on an F-D index under strong noise variable working condition interference. Comprising the steps of vibration signal acquisition of a source domain and a target domain, time-frequency transformation, construction of a time-frequency convolution feature extraction network (AM-TFCN) with an attention mechanism, and construction of a two-stage adversarial migration model composed of a feature extractor, a label classifier and a domain discriminator and an adversarial training strategy based on an F-D index. According to the method, supervised training can be carried out on a feature extractor and a label classifier by utilizing source domain labeling data, then parameters are migrated, F-D index dynamic adjustment is introduced under the constraint of a gradient inversion layer, the problems of distribution alignment and fault category identification from a non-interference working condition to different noise levels and variable working condition target domains are solved, and the fault classification accuracy is improved. The migration diagnosis precision is remarkably improved, the result fluctuation is reduced, and the two-stage anti-migration fault diagnosis method based on the F-D index is effective.

The invention relates to the technical field of fault diagnosis, and discloses an exciting transformer fault diagnosis method based on a liquid neural network and attention, and the method comprises the steps: obtaining the modal data of an exciting transformer, carrying out the preprocessing of the modal data, and generating a standardized input tensor; the modal data comprises vibration modal data, temperature modal data and partial discharge modal data; inputting the standardized input tensor into a liquid neural network model, wherein the liquid neural network model comprises an input layer, a liquid layer and an output layer; wherein in the liquid layer, liquid neurons and an attention mechanism module are arranged, time sequence features of modal data are extracted through the liquid neurons, multi-modal feature weighted fusion is carried out in combination with the attention mechanism module, fusion features are generated, and the fusion features are transmitted to the output layer; and determining the fault category of the exciting transformer according to the classification result of the output layer. Accurate recognition of early weak faults of the exciting transformer is achieved, and the real-time performance and robustness of fault diagnosis are improved.

An axle box bearing fault diagnosis method based on residual errors and data scaling comprises the steps that original one-dimensional vibration signals of an axle box bearing are collected, and samples of standardized signals are obtained after preprocessing; features sensitive to faults are extracted to form fault state features, then standardization processing is carried out, and standardized fault features are obtained; a Transform encoder time sequence prediction model based on an attention mechanism is constructed to serve as a reference model; inputting the standardized fault features into the reference model, and calculating a training fault residual error between a model prediction value and an actual observation value; carrying out local random scaling on the training fault residual error to construct a mixed training set, and training a classification model by using the mixed training set; and obtaining a residual error corresponding to a to-be-detected vibration signal, inputting the residual error corresponding to the to-be-detected vibration signal into the trained classification model, and finally outputting a fault category. The method can improve the generalization insensitivity of the model to the fault degree, and achieves the robust recognition of a wide-range unknown-size fault.

The present application relates to the field of fan fault diagnosis, and particularly relates to a small sample fault classification method and system for a gear box, which comprises the following steps: introducing a self-attention mechanism into a network structure of DCGAN, denoted as SA-DCGAN i training the SA-DCGAN, and using the SA-DCGAN to generate a plurality of samples Y of different fault types, thereby forming a sample data set Y i ; pre-training a dense convolutional network model using Y i , obtaining a source domain model; constructing a target domain model, migrating the parameters of the source domain model to the target domain model, fine-tuning the target domain model using gear box vibration signal data X, and taking the fine-tuned target domain model as a fault classification model; inputting a gear box vibration signal of a fault class to be diagnosed into the fault classification model, obtaining a corresponding small sample fault classification result of the gear box, and accurately diagnosing the fault type of the gear box under the condition of a small sample, and achieving a better fault classification effect.

The invention discloses a communication equipment fault automatic diagnosis system and method based on artificial intelligence, and the system comprises a data collection and fusion module which is used for collecting multi-mode operation and maintenance data from communication equipment; the anomaly detection and feature extraction module is used for carrying out anomaly detection and log feature extraction; the multi-modal fault diagnosis engine is used for analyzing the fused multi-modal features by adopting a graph neural network model and outputting a diagnosis report containing fault categories and root cause positioning; the repair strategy generation and verification module is used for matching the repair strategy from the knowledge base based on the diagnosis report and verifying the repair strategy in the security sandbox; the automatic execution and feedback module executes a strategy passing verification and feeds back a result to optimize the model and the knowledge base, accurate diagnosis of faults is achieved through multi-modal data fusion and intelligent analysis, the reliability of automatic repair is guaranteed through a safety verification mechanism, and the reliability of fault diagnosis is improved. And the automation level and the fault handling efficiency of network operation and maintenance are obviously improved.

This invention relates to the field of industrial fault diagnosis technology and discloses a federated incremental mechanical fault diagnosis method in a dynamic client environment. The method includes: constructing a federated learning system composed of a cloud server and a dynamic client, configuring a shallow neural network, a sample dataset, a fault diagnosis model, and a local memory; training the fault diagnosis model on the client and storing prototype samples corresponding to historical fault categories in the local memory; applying perturbation to the prototype samples, inputting them into the shallow neural network to calculate gradient vectors, and uploading these vectors, along with model parameters and network parameters, from the client to the cloud server; performing gradient inversion iterations on the cloud server to construct a validation set to evaluate model performance, and calculating the aggregate weights of each model based on the validation loss, thereby constructing a global diagnostic model and distributing it to the client to achieve real-time fault diagnosis of mechanical equipment. This invention achieves collaborative, continuous, and high-precision mechanical fault diagnosis across distributed dynamic clients.

The application belongs to the technical field of equipment management, and discloses a nuclear detector fault diagnosis method, device, computer equipment and storage medium. The nuclear detector fault diagnosis method comprises the following steps: acquiring an output signal of the nuclear detector; inputting the output signal into a pre-trained deep belief network for fault identification to obtain a fault parameter corresponding to the output signal; and adopting a generalized likelihood ratio test classification model to perform fault classification on the fault parameter to obtain a fault category corresponding to the output signal, so that the fault of the nuclear detector can be quickly and accurately detected.

The invention provides a fault diagnosis method for open set domain generalization under a continuous variable working condition. The method comprises the following steps: firstly, constructing a training sample with time domain data, a working condition index and a fault category index; converting the time domain data into a time-frequency domain and extracting semantic features; a class-specific semantic reconstruction module is adopted to classify the semantic features; a cross-domain alignment module is adopted to estimate mutual information of the semantic features of the training samples and the working condition indexes, and the smaller the mutual information is, the lower the working condition dependence degree is; constructing joint loss including cross-domain alignment loss; the cross-domain alignment loss constrains the feature distribution consistency between continuous domains based on mutual information minimization; back propagation training is carried out by adopting joint loss, so that the class-specific semantic reconstruction module learns and extracts the working condition invariant feature extraction capability; and performing fault classification by using the trained feature extraction module and the class-specific semantic reconstruction module. According to the method, fault diagnosis can be realized under a cross-continuous change working condition, and meanwhile, the method has the capability of identifying unknown fault types.

This application relates to a method and system for fault monitoring of thermal flow sensors based on multi-source data fusion. The method includes: dividing historical operating status data at various time points into multiple data subsets according to a preset time window; updating a first graph object based on the features of each graph node and the correlation strength of edge features corresponding to the data subsets to construct a time-series subgraph corresponding to multiple fault category labels and training a first defect detection model; dividing current operating status data into a time-series subgraph to be detected corresponding to multiple data subsets to be detected according to a preset time window; inputting the time-series subgraph to be detected into the first defect detection model to determine the preliminary fault detection result of the thermal flow sensor to be detected; and correcting the first fault category based on domain knowledge graph and text feature data to determine a second fault category and its corresponding natural language description. This application can accurately identify complex faults caused by multi-parameter collaboration.

The application discloses a bearing continuous fault detection method and system of a replay enhancement prototype network, which first carries out pretreatment based on rolling bearing fault data. Secondly, a time sequence feature extractor and a category feature modeling system are constructed to obtain an initial diagnosis model. Based on the pretreated fault data, the initial diagnosis model is trained through a joint loss function for known fault categories. The detected sample is input into the trained initial diagnosis model to determine whether it is a new category. The detected new category is taken as an object to update a category prototype library based on a prototype expansion mechanism, and an incremental training data set is constructed to realize incremental iterative optimization. Finally, based on the test set data of the new category and the known category, the fault detection result of the REIPN model after incremental optimization is output, and performance evaluation is carried out. The application significantly reduces the time cost of retraining and breaks through the limitations of traditional fault diagnosis models in the expansion of new categories.

A machine learning-driven system for pattern deviation detection and autonomous correction of data integrity errors in digital networks, comprising: (a) a data acquisition interface configured to receive digital data elements from a variety of network-connected sources and to associate the digital data elements with source metadata; (b) a preprocessing and feature extraction engine configured to transform the digital data elements into feature representations that include at least structural features, temporal features, protocol compliance features, and origin features; (c) a pattern modeling engine configured to maintain a reference behavior representation derived from baseline-consistent feature representations;(d) an anomaly scoring engine configured to calculate a composite anomaly score for an incoming feature representation based on at least one reconstruction deviation and one consistency deviation relative to the reference behavior representation; (e) a fault classification engine configured to assign an integrity fault category and an affected area, which may include a field, record, packet, block, or stream segment; (f) a corrective orchestration engine configured to generate and evaluate a variety of corrective candidates and select a corrective action according to a confidence score derived from the agreement of the fault category, the origin state, the source trustworthiness, and the expected validation success;(g) a validation engine configured to check a corrected data element against integrity constraints; (h) a rollback control configured to restore a saved previous state if the check fails; and (i) an audit log generator configured to record artifacts relating to anomalies, corrections, validations, and rollbacks.

The present application relates to the technical field of fault detection, and relates to a relay contact fault identification method based on wavelet denoising and a support vector machine, comprising the following steps: S1, constructing a current signal acquisition and double-channel reconstruction circuit: a Rogowski coil is sleeved on an output loop of a relay contact, and a differential voltage signal is inducted and output by the Rogowski coil; S2, respectively adopting a first wavelet base and a second wavelet base to perform multi-scale wavelet decomposition, threshold denoising and wavelet reconstruction on a complete current digital sequence and a transient current digital sequence; S3, extracting time domain statistical features from the complete current digital sequence after denoising, extracting time-frequency energy features from the transient current digital sequence after denoising, and outputting a fault category of the relay contact after classification and decision by a support vector machine classifier. The present application overcomes the inherent contradiction that a single acquisition channel is difficult to simultaneously consider slow-changing contact components and transient disturbance components in terms of dynamic range and denoising strategy.

The application provides a multi-dimensional transformer fault diagnosis and state evaluation system and method, and belongs to the technical field of transformer fault diagnosis.The multi-dimensional transformer fault diagnosis and state evaluation system comprises a receiving module, a classification module, a diagnosis module, an analysis module and an evaluation module.The receiving module is used for receiving historical operation parameters of the transformer uploaded by a user and analyzing fault characteristic parameter frequencies.The classification module is used for receiving current operation parameters of the transformer and extracting suspected category characteristic parameters.The diagnosis module is used for comparing the suspected category characteristic parameters with the fault characteristic parameter frequencies to determine fault category characteristic parameters.The analysis module is used for determining fault levels of the fault category characteristic parameters.The application comprehensively judges the health state of the transformer, thereby comprehensively evaluating the fault of the transformer, so that maintenance personnel can formulate corresponding maintenance measures.

The invention discloses a distribution line fault detection and classification method based on a back propagation neural network, and the method comprises the steps: respectively obtaining a voltage measurement value and a current measurement value before and after a fault of a distribution line, carrying out the data preprocessing of the voltage measurement value and the current measurement value, and obtaining selected data for data training, and inputting the selected data into a pre-trained back propagation neural network for data training, extracting fault features according to a training result and analyzing a fault rule, and mapping the fault features to corresponding fault types according to a fault rule analysis result to classify the fault features, thereby obtaining a fault classification result. And obtaining a detection and classification result of the current distribution line fault. The method has the advantages that the fault features are accurately mapped to correct fault types or position output, and the accuracy of fault detection and classification is improved.

The invention discloses a train transmission motor, gearbox and axle box fault diagnosis method and system, and belongs to the technical field of train transmission system fault diagnosis, and the method comprises the steps: designing special expert networks for key components of a train transmission system; a multi-head self-attention gating network is introduced, and features output by the special expert network are dynamically fused; a joint loss function including cross entropy loss, KL divergence regularization loss and load balancing loss is adopted to train a special expert network and a gating network; based on a teacher-student model architecture and knowledge distillation, newly added fault category adaptation and historical knowledge reservation are realized; and inputting a to-be-diagnosed multi-component vibration signal into the trained model, and outputting a fault category with the highest probability as a diagnosis result through feature extraction, gating network fusion and classifier prediction. According to the method, the problems of multi-component independent modeling, incremental learning delay, catastrophic forgetting, poor multi-working-condition adaptability and the like in train transmission system fault diagnosis are solved.

The application discloses a fault diagnosis method and system based on hard verification and an evolvable graph neural network, which obtains a final category cluster set through unique mode hard verification and mixed mode hard verification, and trains an expert model for each category cluster in the final category cluster set; then, a graph neural network is trained by using historical samples and the expert models; and then, a new category cluster is added by using real-time samples, the expert models and the graph neural network, and fault classification is performed. The application can autonomously and reliably discover new fault types under unsupervised conditions, seamlessly integrates the newly discovered fault types into a diagnosis model, realizes dynamic expansion of a model structure and self-evolution of a knowledge system, and has high robustness and adaptability to working condition changes.

The invention provides a fault diagnosis method for a gas-steam circulating water pump, a medium and equipment, and the method comprises the steps: constructing a feature encoder and a feature enhancement module based on a convolutional neural network, introducing a cross attention mechanism, and achieving the deep fusion of multi-modal features. According to the scheme, a multi-task learning framework is adopted, all task output heads independently generate corresponding diagnosis results, and finally a comprehensive fault diagnosis report containing the fault position, the fault degree and the fault category is formed through integration. The multi-task architecture not only realizes cross-task knowledge sharing and complementation, but also further improves the accuracy and comprehensiveness of diagnosis. The invention further designs a fault diagnosis system and device matched with the method, the trained model can be efficiently deployed on edge equipment, and real-time, multi-dimensional, multi-granularity and extensible fault diagnosis is achieved.

This invention provides a method and system for enhancing and diagnosing equipment fault data based on a diffusion model, belonging to the interdisciplinary field of electrical equipment fault diagnosis and artificial intelligence. It aims to solve the problem of data imbalance. This invention proposes using a limited number of samples to train a designed conditional denoising diffusion model, thereby generating pseudo-samples of any specified fault category, achieving sample expansion. During the forward diffusion noise addition process, the conditional denoising diffusion model defines a regional importance weight matrix, making the model focus more on important fault feature regions and suppressing information redundancy regions. Furthermore, through a noise-aware classifier, its gradient is used to impose conditional constraints on the pre-trained diffusion model, further guiding the diffusion model to generate the required category images. This enables the efficient generation of a large amount of high-fidelity, highly diverse synthetic fault data even when fault samples are scarce, solving the problem of data scarcity and imbalance in industrial scenarios.