5 results about "Order tracking" patented technology

The present application relates to a method for detecting loosening of ceramic dewatering elements for papermaking based on vibration spectrum analysis, aiming to solve the problems of difficult early identification of loosening and weak anti-working condition interference ability of ceramic dewatering elements in the running of the paper machine wire section. The scheme synchronously collects vibration signals and multi-dimensional working condition parameters, generates a structured frequency spectrum through time-frequency analysis and power spectrum density processing, and then fuses the knowledge base to match the reference structure template, realizing the topological edit distance quantization of the measured and reference frequency spectrum. Combined with the fuzzy rule engine, the different loosening degrees are dynamically weighted and inferred, and the positioning accuracy is improved through graph structure evolution trajectory analysis and high-resolution order tracking. The technology has high robustness and early loosening identification ability, which is beneficial to improve the intelligentization and intelligent decision level of the health monitoring of the clean production line ceramic elements.

This invention discloses a method and system for monitoring equipment status based on vibration signals, relating to the field of mechanical equipment life prediction technology. The invention simultaneously collects vibration and temperature signals; employs adaptive variational mode decomposition to extract degradation-sensitive features; estimates instantaneous rotational speed based on the vibration signal itself, converting it into angular domain signals through order tracking; constructs an overcomplete dictionary for sparse decomposition, automatically determining the first prediction time based on the sparse energy ratio; fuses multimodal features to construct normalized health indicators; uses a temporal fusion prediction network to predict remaining life and generate graded maintenance recommendations; and continuously optimizes the model through incremental learning. This invention, through the synergy of adaptive decomposition, rotational speed-free order tracking, angular domain sparse decomposition, and lightweight networks, achieves accurate remaining life prediction without the need for speed sensors and a large number of fault samples, significantly improving early fault warning capabilities and the reliability of predictive maintenance decisions under varying load conditions.

The present application relates to the technical field of fan bearing fault diagnosis, in particular to a fan bearing fault online detection method and system, adopting a weighted combination envelope spectrum technology, quantifying the correlation of fault characteristics and resonance frequency bands through two-dimensional spectrum coherence, combining the frequency domain signal-to-noise ratio to adaptively assign weights, aggregating and enhancing the fault information of multiple resonance frequency bands, effectively offsetting the attenuation effect in the signal transmission process, solving the problem of signal attenuation distortion caused by magnetic attraction installation through the machine shell under the limitation of fan structure size. The order tracking and constant angle increment sampling technology is introduced, the corresponding relationship between the vibration signal and the bearing rotation angle is established, the time domain signal is converted into the angle domain signal, the fault characteristic frequency and the rotating speed are decoupled, the theoretical fault frequency of the inner ring, the outer ring, the roller and other components is accurately matched with the detection result, the detection adaptability under variable working conditions is greatly improved, compared with the fixed frequency domain analysis of the traditional acceleration envelope method, the misjudgment caused by frequency offset is effectively avoided.

Systems and methods that use a trained multi-tenant model are disclosed herein. In an embodiment, a method of using a trained multi-tenant model in a workspace includes receiving a first video feed including a workstation and a second video feed including a completed item station, building a worker utilization inference result queue from a first asynchronous inference thread that runs a human pose estimation model and a clothing color extraction model using sequential frames from the first video feed, building an order tracking inference result queue from a second asynchronous inference thread that runs an object detection model and an item tracking model using sequential frames from the second video feed, and merging the worker utilization inference result queue and the order tracking inference result queue to generate a real-time indication of activity at one or more workstations shown in the first video feed or the second video feed.

A bondless phase-order tracking method based on deep time-frequency refinement includes: 1) constructing several non-stationary simulated vibration signals; 2) constructing an ideal time-frequency binary mask; 3) constructing a multi-scale one-dimensional complex-valued convolution module to perform generalized time-frequency transformation on the non-stationary simulated vibration signals to obtain an initial two-dimensional time-frequency representation; 4) constructing a deep time-frequency refinement model, using the initial two-dimensional time-frequency representation as input and the ideal time-frequency binary mask as supervision labels to construct a dataset; 5) inputting the initial two-dimensional time-frequency representation into the deep time-frequency refinement model to obtain prediction data, and back-optimizing the deep time-frequency refinement model based on a loss function until a trained deep time-frequency refinement model is obtained; 6) acquiring one-dimensional vibration signals during the actual operation of rotating machinery and inputting them into the trained deep time-frequency refinement model to obtain a refined time-frequency representation, thereby obtaining the order tracking result. This invention provides a more stable and efficient signal analysis method.