4 results about "Probabilistic classification" patented technology

Embodiments described herein provide a probabilistic approach using a probabilistic classifier to detect anomalous categorical data, e.g., data points in non-numerical data fields. Specifically, the probabilistic classifier may generate a conditional probability distribution of a data attribute, such as the “country of origin” field in a bond data record, conditioned on various other data attributes of the bond data record obtained from different databases. The generated conditional probability corresponding to the data attribute of the store non-numerical value, e.g., the “country of origin” equals a certain country code as stored, may indicate whether a data anomaly exists with the stored value for this particular data attribute. When an anomaly is detected with a data attribute, the categorical value associated with the highest conditional probability corresponding to the data attribute may be provided as the suggested value for the data attribute.

The application discloses a kind of industrial equipment residual life prediction method and system based on bayesian updating, the prior model of equipment degradation process is established by historical failure data, and initial parameters are obtained using maximum likelihood estimation;Collect the condition monitoring data of equipment, after feature extraction and normalization processing, form the observation value of degradation amount;Using particle filtering algorithm and bayes theorem, the posterior distribution of degradation model parameters is recursively updated;Through Markov chain Monte Carlo sampling technology, the point estimate, confidence interval and failure probability density curve of residual life are output;According to the prediction result, dynamically adjust equipment maintenance plan, and trigger early warning when failure probability exceeds preset threshold, provide decision support for predictive maintenance;The system corresponds and contains data acquisition and pretreatment, prior model construction and the like module.The application is fused by probability classification and statistical inference, improves prediction accuracy, and provides reliable decision basis for equipment predictive maintenance.

This invention discloses a multi-feature fusion fault diagnosis method for vibration signals of a vehicle axle assembly, belonging to the field of mechanical fault diagnosis technology. The method includes acquiring and preprocessing the raw vibration signals of the vehicle axle assembly. Time-domain, frequency-domain, and time-frequency-domain features are extracted from the preprocessed signals to form a primary feature set. A subset of sensitive features is selected through redundancy analysis and importance ranking. A kernel function is used to map the sensitive features to a high-dimensional space and perform linear reconstruction oriented towards fault categories, resulting in a more discriminative fusion feature vector. This fusion feature is input into a pre-trained probabilistic classification model to calculate the posterior probability of belonging to each fault mode. The fault mode is determined based on the probability, and a diagnostic result is generated. This invention enhances the discriminative ability of fault features through kernel space feature reconstruction and provides a reliability assessment of the diagnostic results using probability output, thereby improving the accuracy of fault diagnosis and the level of decision support.

The application discloses a photovoltaic power generation power prediction method, which comprises data preprocessing, weather type probability classification, similar day synthesis and multi-view interactive parallel prediction; the method constructs a probability classification model to divide the sample data by weather types, and uses the uncertain information of weather division as a confidence weight to calibrate the model input features; the design uses physical priori to guide feature enhancement, solves the problem that the existing method is prone to feature distortion under extreme weather conditions and the model generalization ability is limited; in addition, the introduction of physical priori to guide feature enhancement enriches the information of photovoltaic data, suppresses the interference of noise or fuzzy samples, significantly improves the utilization efficiency of information, guides the prediction model to pay attention to samples with high confidence, and enhances the generalization ability of the model.