VIP20

本课程属于：005-Artificial Intelligence and Machine Learning Engineering English Technical Courses · 共 20 门相关课程，组合学习更完整。

查看所属VIP全部课程 →

← 返回所属目录咨询 / 购买

End-to-End Predictive Modeling for Industrial Sensor Data

30 Chapters

View Course Samples Online

Industrial sensor data landscape

sensor types, sampling rates, signal characteristics, and predictive modeling objectives in manufacturing, energy, and process industries.

Industriallandscape

End-to-end predictive modeling lifecycle

problem framing, data acquisition, preprocessing, feature engineering, model training, deployment, and monitoring for industrial sensor applications.

End-to-endpredictive

Sensor data acquisition and ingestion

OPC UA, MQTT, Modbus, historians, edge gateways, and time-series databases for collecting high-frequency industrial data.

acquisitioningestion

Exploratory data analysis for sensor signals

time-domain statistics, spectral analysis, trend decomposition, and anomaly visualization using real industrial datasets.

Exploratoryanalysis

Data quality assessment and cleaning

handling missing values, outliers, sensor drift, stuck-at faults, and noise in continuous industrial sensor streams.

assessmentcleaning

Time-series preprocessing techniques

resampling, synchronization, windowing, normalization, and segmentation for multivariate sensor data.

Time-seriespreprocessing

Feature engineering for industrial sensor data

statistical, spectral, wavelet, and time-series features for predictive maintenance and quality prediction.

industrialstatistical

Domain-driven feature extraction

physics-based indicators, operating condition segmentation, and expert knowledge integration for sensor-based modeling.

Domain-drivenextraction

Dimensionality reduction and feature selection

PCA, autoencoders, mutual information, and wrapper methods for high-dimensional sensor data.

Dimensionalityreduction

Predictive modeling fundamentals for sensor data

regression, classification, and anomaly detection formulations with industrial case examples.

Predictivemodeling

Classical machine learning for sensor data

random forests, gradient boosting, SVMs, and their application to fault detection and remaining useful life estimation.

Classicalgradient

Deep learning for time-series sensor data

CNNs, RNNs, LSTMs, GRUs, and temporal convolutional networks for industrial prediction tasks.

time-seriesCNNs

Sequence-to-sequence and attention-based models

transformers, temporal fusion transformers, and their use in multi-horizon industrial forecasting.

Sequence-to-sequenceattention-based

Anomaly detection and fault diagnosis

unsupervised, semi-supervised, and supervised approaches for industrial sensor anomaly detection.

detectiondiagnosis

Remaining useful life (RUL) prediction

degradation modeling, health indicators, and deep learning approaches using run-to-failure sensor data.

Predictive maintenance modeling

failure classification, early warning systems, and maintenance scheduling based on sensor-driven predictions.

Predictivemaintenance

Soft sensing and virtual sensors

inferring hard-to-measure quality variables from correlated sensor readings in process industries.

inferringhard-to-measure

Model validation and evaluation for industrial sensor data

time-series cross-validation, backtesting, and domain-specific metrics like false alarm rates.

validationevaluation

Handling non-stationarity and concept drift

adaptive models, online learning, and drift detection in evolving industrial environments.

Handlingnon-stationarity

Imbalanced and rare event modeling

techniques for fault prediction when failures are infrequent, including resampling, cost-sensitive learning, and anomaly scoring.

Imbalancedmodeling

Model interpretability and explainability

SHAP, LIME, attention visualization, and feature importance for trust in industrial predictive models.

interpretabilityexplainability

Deployment of predictive models on edge and cloud

model compression, ONNX, TensorFlow Lite, edge inference, and integration with SCADA/MES systems.

Deploymentpredictive

MLOps for industrial predictive modeling

CI/CD pipelines, model versioning, monitoring, retraining triggers, and data drift detection in production.

MLOpsindustrial

Real-time inference and streaming analytics

Kafka, Flink, Spark Streaming, and low-latency prediction for online industrial monitoring.

Real-timeinference

predictive maintenance in rotating machinery using vibration and acoustic sensor data.

predictivemaintenance

quality prediction in semiconductor manufacturing using multivariate sensor time series.

predictionsemiconductor

anomaly detection in oil and gas pipelines using pressure, flow, and temperature sensors.

detectionpipelines

energy load forecasting in smart grids using smart meter and weather sensor data.

fault detection in wind turbines using SCADA sensor data and deep learning.

detectionturbines

Capstone project

end-to-end predictive modeling pipeline for a real industrial sensor dataset, from data ingestion to deployment and monitoring.

Capstoneend-to-end