Patent classifications
G01M5/0041
Derivation Method, Derivation Device, Derivation System, And Program
A first index value, which is an index value of a deflection amount of a structure generated at an observation point, and a second index value, which is an index value of a deflection amount at a designated position in the structure, are acquired based on the number of moving objects formed in a formation moving object, an entry time point, an exit time point, and environment information. An estimated value of the deflection amount of the structure at the designated position is derived based on time-series data measured at the observation point, the first index value, and the second index value.
Fiber-optic sensors in a rosette or rosette-like pattern for structure monitoring
An apparatus, and related method, relates generally to a fiber-optic sensing system. In such a system, fiber-optic sensors are in a rosette or rosette-like pattern. An optical circulator is coupled to receive a light signal from a broadband light source, to provide the light signal to the fiber-optic sensors, and to receive a returned optical signal from the fiber-optic sensors. A spectral engine is coupled to the optical circulator to receive the returned optical signal and configured to provide an output signal.
DETERMINING REMAINING LIFE OF A HOSE ASSEMBLY BASED ON SENSOR DATA
A system may include a hose assembly and a controller. The hose assembly may comprise a plurality of sensor devices configured to generate sensor data regarding the hose assembly. The sensor data may include at least one of first sensor data regarding a bend radius of a first portion of the hose assembly, or second sensor data regarding an amount of torque at a second portion of the hose assembly. The controller may be configured to receive the sensor data from the plurality of sensor devices; determine a remaining life of the hose assembly based on the sensor data; and perform an action based on the remaining life of the hose assembly.
PRESSURE VESSEL STRAIN ANALYSIS DEVICE AND PRESSURE VESSEL MANUFACTURING METHOD
Provided is a pressure vessel strain analysis device capable of grasping a correlation between manufacturing conditions and strains. The pressure vessel strain analysis device includes an analysis unit. Based on a plurality of manufacturing conditions of a plurality of pressure vessels and a plurality of strains acquired by an image correlation method in a state where a predetermined internal pressure is applied to the plurality of pressure vessels manufactured under the plurality of manufacturing conditions, the analysis unit calculates a correlation between the plurality of manufacturing conditions and the plurality of strains.
A METHOD AND A SYSTEM FOR TRACKING MOTION OF A BLADE
The present invention relates to a method and a system for tracking the motion of a blade of a wind turbine. One embodiment relates to a blade motion tracking system for installation on a wind turbine blade, where the wind turbine blade comprises a blade root and a blade tip. The system comprises at least one light module comprising at least a first light source, preferably adapted to emit light in the direction of the blade root. An optical measuring device is provided, preferably located at the blade root, adapted to receive light emitted from the first light source(s). The optical measuring device is preferably a position sensitive detector identifying the position of the first light source relative to the position sensitive detector. A single light source located at the tip of the blade, close to the tip of the or towards the tip of the blade, is sufficient to measure deflection of the blade. Advantageously the first light source is modulated with a predefined modulation frequency such that light from the first light source can be distinguished from ambient light and thereby minimize the influence of the ambient light conditions during detection.
COMPUTER-ASSISTED METHOD AND SYSTEM FOR DETERMINING AND VISUALISING FORCE FLOWS IN A SCAFFOLD
The invention relates to a system and method for determining and visualizing force flows in a bar supporting structure (1), which is preferably in the form of scaffolding, comprising a plurality of ladder or strut elements (5a-5c) which extend vertically and are set up in a disputed manner relative to one another and which are detachably connected via scaffolding couplings (7) to strut elements (6a-6d) extending diagonally and/or horizontally transversely thereto, wherein at least a load-critical part of the support elements (5a-5c) and/or strut elements (6a-6d) and/or scaffold couplings (7) of the bar structure (1) are provided with load sensors (8a-8c) for detecting static operating load values, the measured values of which are analysed in real time by a downstream analysis unit (9) for evaluating current load situations.
CONTRASTIVE LEARNING OF UTILITY POLE REPRESENTATIONS FROM DISTRIBUTED ACOUSTIC SENSING SIGNALS
A testing procedure including a data collection procedure and a contrastive learning-based approach, for establishing a profile for utility poles surveyed in an embedding space. Unique properties of utility poles are preserved in a low-dimensional feature vector. Similarities between pairs of samples collected at the same or different poles is reflected by the Euclidean distance between the pole embeddings. During data collection—variabilities of excitation signals are manually introduced, e.g. impact strength, impact locations, impact time ambiguity, data collecting location ambiguity on a DFOS/DAS optical sensor fiber/cable. Data so collected provides a learned model learned complete information about a utility pole and is more robust with respect to uncontrollable factors during operation. A model training procedure that effectively extracts a utility pole intrinsic properties (e.g., structure integrity, dimensions, structure variety) and remote extrinsic influence (e.g., excitation strength, weather conditions, road traffic), without knowing the ground truth of these factors. The only identifying label required is an ID of any tested poles, which is readily available. The model is trained adaptively—end-to-end—is advantageously easy-to-implement on modern deep learning frameworks such as PyTorch.
Four-dimensional crane rail measurement
A method and system for conducting a non-contact survey of an overhead crane runway system using a survey apparatus that is alternately located in the crane bay or on a crane bridge girder. Disclosed more particularly are a method and system for testing an overhead crane runway beam 3D alignment or an overhead crane runway rail 3D alignment using a 3D laser scanner.
Fiber optic load sensors and systems therefor
A load sensing system for sensing a load on a structure can include an optical load sensing element configured to change an optical state based on a force applied thereto, an optical source operatively connected to the optical load sensing element and configured to input an input optical signal to the optical load element, and an optical detector configured to receive a returned optical signal from the optical load sensing element. The optical detector can be configured to detect one or more frequency peaks of the returned optical signal and to use the one or more frequency peaks of the returned optical signal to correlate to a load value of the load and output the load value indicative of the load.
Apparatus and method for predicting a deformed shape of a structure
An apparatus for predicting a deformed shape of a structure, and method of operating and forming the same. In an embodiment, the apparatus is formed with a riser with ends coupled respectively to an upper location and a lower location. The apparatus includes sensors configured to sense an inclination of the riser at riser nodes positioned on the riser between the upper location and the lower location. The apparatus further includes a data processing system configured to produce an estimate of a deformed shape of the riser employing deformed shaped functions to represent, respectively, the estimate of the deformed shape of the riser above and below the riser nodes.