An irregularity detection apparatus (100) converts a multi-valued-signal value of each of one or more multi-valued-signals at each time point into a binary-signal-value group. The irregularity detection apparatus calculates a forecast-signal-value group at a subject time point by computing a forecast model with use of, as input, a past-signal-value group which is a collection of a binary-signal value of each of one or more binary signals at each past time point and the binary-signal-value group of each of the one or more multi-valued signals at each past time point. The irregularity detection apparatus compares with the forecast-signal-value group, a collection of the binary-signal value of each of the one or more binary signals at the subject time point and the binary-signal-value group of each of the one or more multi-valued signals at the subject time point, and determines a state of a subject system (220) at the subject time point.

A method and system for monitoring a rail vehicle are provided. The method includes: a local device acquires current operating data and historical operating data of a rail vehicle from a controller of the rail vehicle, herein the local device, which is connected with the controller, is a portable testing device; the local device obtains fault information according to the current operating data and the historical operating data, herein the fault information includes at least one of a fault code and a fault description; the local device obtains a maintenance plan corresponding to the fault information, herein the maintenance plan is obtained through a remote device or a server side; and the local device maintains the rail vehicle according to the maintenance plan. The disclosure solves a technical problem in the traditional art that a troubleshooting process of a rail vehicle is difficult, and when the rail vehicle has a fault, the fault cannot be solved timely.

A method of detecting abnormalities includes: calculating a reference failure rate using failure data at a plurality of points in time included in a particular period; calculating a detection failure rate and weighting, corresponding to failure data at a detection time point after the particular period, using the reference failure rate; calculating an abnormality index based on multiplying the detection failure rate by the weighting; comparing the abnormality index with an index corresponding to a control limit for stably controlling a failure rate; and detecting whether the failure data at the detection time point is abnormal, based on a result of the comparison of the abnormality index with the index corresponding to the control limit.

An apparatus for detecting a fault state of an aircraft is provided. The apparatus accesses a training set of flight data for the aircraft. The training set includes observations of the flight data, each observation of the flight data includes measurements of properties selected and transformed into a set of features. The apparatus builds a generative adversarial network including a generative model and a discriminative model using the training set and the set of features, and builds an anomaly detection model to predict the fault state of the aircraft. The anomaly detection model is trained using the training set of flight data, simulated flight data generated by the generative model, and a subset of features from the set of features. The apparatus deploys the anomaly detection model to predict the fault state of the aircraft using additional observations of the flight data.

Methods and systems are provided for increasing an accuracy of anomaly detection in assets such as vehicle components. In one example, a method provides for continuous health monitoring of connected physical assets, comprising adapting thresholds for anomaly detection and root cause analysis algorithms for the connected assets based on an aggregation of new connected data using machine learning; updating and ranking advanced statistical and machine learning models based on their performance using connected data until confirming a best performing model; and deploying the best performing model to monitor the connected physical assets.

A communication apparatus includes a communication unit, a power supply unit, a temperature monitoring unit, and a control unit. The communication unit communicates with an additional apparatus. The power supply unit supplies power to components mounted in the communication apparatus. The temperature monitoring unit monitors a temperature in the communication apparatus to detect presence or absence of a temperature abnormality. In a case where the temperature monitoring unit detects the temperature abnormality, the control unit performs power supply stop processing of stopping the power supply from the power supply unit to at least some of the components. Furthermore, the control unit stops or decreases communication of the communication unit in a case where the temperature monitoring unit detects the temperature abnormality.

A vehicle consumables management system includes a consumables remaining amount calculation unit receiving vehicle data including a brake pedal input signal, an outdoor temperature, a driving distance, a wheel velocity, and a wheel speed such as a wheel RPM, and calculating a remaining amount of a tire tread based on the driving distance and the wheel speed, and/or calculating a remaining amount of a brake pad based on at least one of the brake pedal input signal and vehicle acceleration and/or deceleration information, thereby being capable of accurately detecting the remaining amount of the tire tread of the vehicle without assistance of separate inspection equipment, and accurately predicting a wear amount and a remaining amount of the brake pad of the vehicle without additional expensive equipment.

An online monitoring device of 3D printing equipment includes a signal collection module, a signal processing module, a feature extraction module, a monitoring module and a knowledge base module. A vibration signal of a preset component of the 3D printing equipment is collected by a vibration sensor. The collected vibration signal of each preset component is converted from an analog signal to a digital signal and the spectrum characteristics are extracted. Based on the spectrum characteristics of each preset component, the operation state type of the preset component is obtained by a comparative analysis model. The knowledge base module is configured to store newly added samples and initial samples of the 3D printing equipment. The initial samples include spectrum characteristic information and corresponding fault category of known faults, and the newly added samples include spectrum characteristic information and corresponding fault category of new faults.

The present disclosure provides a method for gas meter replacement prompt based on a smart gas Internet of Things and a system thereof. The method is applied to a sub platform of a management platform of a smart gas indoor device, wherein the method includes: obtaining model data, use data, and maintenance data of a target gas meter in a smart gas data center; determining a target time for replacing the target gas meter and uploading the target time to the smart gas data center based on the model data, use data and maintenance data of the target gas meter, wherein the smart gas data center is configured to send the target time to a smart gas service platform, and the smart gas service platform is configured to send the target time to a smart gas user platform.

Efficient and effective workspace condition analysis systems and methods are presented. In one embodiment, a method comprises: accessing information associated with an activity space, including information on a newly discovered previously unmodeled entity; analyzing the activity information, including activity information associated with the previously unmodeled entity; forwarding feedback on the results of the analysis, including analysis results for the updated modeled information; and utilizing the feedback in a coordinated path plan check process. In one exemplary implementation the coordinated path plan check process comprises: creating a solid/CAD model including updated modeled information; simulating an activity including the updated modeled information; generating a coordinated path plan for entities in the activity space; and testing the coordinated path plan. The coordinated path plan check process can be a success. The analyzing can include automatic identification of potential collision points for a first actor, including potential collision points with the newly discovered object. The newly discovered previously unmodeled entity interferes with an actor from performing an activity. The newly discovered object is a portion of a tool component of a product.