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Anomaly Detection for Industrial Equipment Monitoring

30 Chapters
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01
Industrial equipment monitoring landscape
failure modes, maintenance strategies, and the role of anomaly detection in condition-based maintenance.
Industrialequipment
02
Sensor data acquisition for industrial equipment
vibration, acoustic emission, temperature, pressure, current, and oil debris sensors, sampling rates, and data quality issues.
acquisitionindustrial
03
Data preprocessing and feature engineering for equipment signals
denoising, segmentation, time-domain, frequency-domain, and time-frequency features (FFT, STFT, wavelet, envelope analysis).
preprocessingequipment
04
Statistical anomaly detection methods for equipment monitoring
Gaussian models, PCA, robust covariance, and threshold setting under non-stationary operating conditions.
Statisticaldetection
05
Unsupervised anomaly detection with classical machine learning
k-means, DBSCAN, isolation forest, one-class SVM, and local outlier factor applied to sensor data.
Unsuperviseddetection
06
Supervised learning for fault detection and classification
logistic regression, random forests, gradient boosting, and handling imbalanced fault datasets.
Superviseddetection
07
Deep learning for time-series anomaly detection in equipment
autoencoders, LSTM, GRU, temporal convolutional networks, and transformer-based models.
time-seriesdetection
08
Reconstruction-based and forecasting-based anomaly detection
residual analysis, prediction error thresholds, and sequence-to-sequence models for equipment signals.
Reconstruction-basedforecasting-based
09
Representation learning and self-supervised methods for industrial time series
contrastive learning, masked modeling, and pretraining on unlabeled equipment data.
Representationself-supervised
10
Handling non-stationary and varying operating conditions
operating condition normalization, domain adaptation, and context-aware anomaly detection.
Handlingnon-stationary
11
Multivariate and multimodal anomaly detection
combining vibration, temperature, pressure, and acoustic data, sensor fusion, and cross-channel correlation analysis.
Multivariatemultimodal
12
Anomaly detection for rotating machinery
bearings, gears, shafts, and motors using vibration and current signature analysis.
detectionrotating
13
Anomaly detection for reciprocating and process equipment
pumps, compressors, valves, and heat exchangers using process signals and acoustic emission.
detectionreciprocating
14
Anomaly detection for industrial control systems and SCADA data
telemetry streams, setpoint deviations, and cyber-physical anomaly detection.
detectionindustrial
15
Edge and real-time anomaly detection for equipment monitoring
embedded implementation, model compression, streaming inference, and latency constraints.
real-timedetection
16
Evaluation metrics and validation strategies for equipment anomaly detection
precision, recall, F1, AUC, PR-AUC, early detection, and false alarm management.
Evaluationvalidation
17
Dealing with labeled data scarcity and class imbalance
semi-supervised learning, active learning, data augmentation, and synthetic fault generation.
scarcityimbalance
18
Explainability and interpretability for industrial anomaly detection
feature importance, SHAP, attention maps, and root cause analysis support.
Explainabilityinterpretability
19
Anomaly detection pipelines and MLOps for equipment monitoring
data ingestion, model training, deployment, monitoring, and retraining workflows.
detectionpipelines
20
Case study
anomaly detection for wind turbine gearboxes using SCADA and vibration data.
detectiongearboxes
21
Case study
anomaly detection for industrial pumps in a chemical plant using process and vibration signals.
detectionindustrial
22
Case study
anomaly detection for CNC machine tools using spindle current and acoustic emission.
detectionCNC
23
Case study
anomaly detection for railway vehicle bearings using onboard sensors and edge computing.
detectionbearings
24
Case study
anomaly detection for power transformers using dissolved gas analysis and temperature monitoring.
detectiontransformers
25
Benchmarking and public datasets for industrial equipment anomaly detection
NASA bearing, CWRU, Paderborn, and Tennessee Eastman process.
Benchmarkingdatasets
26
Robustness and adversarial considerations
sensor faults, missing data, noise, and adversarial attacks on anomaly detectors.
Robustnessadversarial
27
Transfer learning and domain generalization across machines and operating conditions
fine-tuning, meta-learning, and few-shot fault detection.
Transfergeneralization
28
Hybrid approaches
combining physics-based models with data-driven anomaly detection for industrial equipment.
approachescombining
29
Deployment challenges and best practices
integration with CMMS, alarm management, operator trust, and maintenance workflow.
Deploymentchallenges
30
Capstone project
end-to-end anomaly detection system for a selected industrial equipment scenario, from data collection to actionable maintenance insights.
Capstoneend-to-end