Intelligent Detection of IoT Sensor Failures: A Practical Guide to Multi-Model Machine Learning Pipelines

This article analyzes an open-source IoT sensor failure classification project. By comparing five algorithms—KNN, Decision Tree, Random Forest, SVM, and Deep Neural Network—it constructs a complete automated failure detection pipeline, providing practical technical references for predictive maintenance of industrial IoT devices.

In industrial IoT (IIoT) environments, sensors are the core of data collection, but failures can lead to data distortion that affects system decisions. Traditional periodic maintenance is costly and inefficient, making machine learning-based predictive maintenance a trend. Sensor failure types include complete failure, accuracy drift, intermittent disconnection, noise anomalies, etc. Accurate identification is of great significance for ensuring production safety and reducing maintenance costs.

The project conducts experimental comparisons of five algorithms:

• KNN: Simple to implement, no training required, but sensitive to high-dimensional data and noise;
• Decision Tree: Good interpretability, easy to understand failure judgment logic;
• Random Forest: Ensemble learning, reduces overfitting, performs robustly in sensor data scenarios with noise;
• SVM: Strong generalization ability in high-dimensional space, can find clear decision boundaries;
• Sequential DNN: Captures non-linear patterns and time-series dependencies.

Sensor data includes time-series, statistical (mean/variance/peak) and frequency-domain features. The preprocessing process includes:

• Missing value handling and outlier detection;
• Feature standardization (eliminating dimensional differences);
• Data splitting (training/validation/test sets);
• Class balance processing (addressing the imbalance of failure samples). These steps ensure models are trained on high-quality data and avoid misjudgments caused by data quality issues.

The project uses accuracy, precision, recall, F1 score, and confusion matrix for unified evaluation:

• Tree-based models (Decision Tree, Random Forest) excel in interpretability and training speed;
• Neural networks are stronger at handling complex non-linear relationships;
• SVM has good generalization ability in small sample scenarios;
• KNN serves as a baseline model to provide performance reference.

The project provides a model selection reference framework for industrial IoT deployment: different scenarios have varying needs (real-time performance, interpretability, high accuracy). Future expansion directions include: introducing LSTM to handle time-series dependencies, combining edge computing to implement on-device inference, and building online learning to adapt to data distribution changes due to sensor aging.

This open-source project demonstrates a systematic approach to solving IoT sensor failure detection. Through multi-model comparison, it not only provides technical implementation references but also establishes a scientific model selection methodology. It is a basic framework worth in-depth study for engineers building predictive maintenance systems.