Cold-Start Motor Condition Monitoring Using Multi-Sensor Signals and Pseudo-Supervised Machine Learning

Summary

Multi-sensor telemetry data (current, voltage, temperature, humidity, vibration) from servo motors are processed using unsupervised structural clustering, isolation-based anomaly detection, and physics-informed risk mapping. This framework outputs deterministic binary pseudo-labels and highly accurate real-time diagnostic states for condition monitoring.

Abstract

As predictive maintenance transitions from the data-centric paradigm of Industry 4.0 to the sustainable, human-centric framework of Industry 5.0, diagnosing servo motor conditions faces the dual challenges of data scarcity and a profound lack of labeled fault samples. To address this cold-start problem, we present a pseudo-supervised machine learning framework evaluated on a custom five-channel dataset comprising 199 servo motor telemetry samples (current, voltage, temperature, humidity, and vibration). The methodology integrates hard structural partitioning (k-means) and soft posterior confidence estimation (Gaussian Mixture Models) to characterize operating modes without prior annotation. Concurrently, an Isolation Forest model quantifies anomaly intensity and establishes a dynamic quantile-based threshold. A critical innovation of this research is the deterministic risk mapping derived from engineering priors; it defines the "high-risk" (abnormal) state by inversely weighting the physical safety margins of the sensors. This mechanism strictly maps unsupervised clusters to binary pseudo-labels. These labels are subsequently used to supervise downstream discriminators (Random Forest and Support Vector Machine). The final online diagnostic outputs a score-level fusion of the classifier probability and the GMM posterior, gated by the anomaly threshold. Quantitative evaluation demonstrates that the Random Forest model achieved a perfect F1 score of 1.000, while the comparative SVM yielded an F1 score of 0.997, proving the framework to be a robust, interpretable, and highly accurate solution for cold-start industrial health monitoring.

Introduction

The reliability of servo motor control systems has historically been a critical performance indicator within industrial automation. As predictive maintenance evolves from the data-centric paradigm of Industry 4.0 towards the sustainable, human-centric framework of industry 5.0, systems must ensure high precision while actively promoting energy efficiency and operational resilience^1,2,3. Faults occurring during long-term ion under harsh environments can lead to catastrophic equipment shutdowns, severe structural damage, and significant economic losses⁴<....

Protocol

1. Experimental setup and data preprocessing

NOTE: The data processing and modeling framework proposed in this study was implemented via custom scripts developed in a high-level programming language, leveraging standard open-source libraries for numerical computation and machine learning. The experimental validation was conducted on a high-performance computational workstation equipped with a multi-core processor and a discrete graphics processing unit (GPU). Specific details regarding all software environments, computational hardware, and sensor components are provided in the Table of Materials. A schematic ....

Results

The proposed framework was validated using a self-collected multi-sensor dataset. After removing incomplete records, the final dataset comprised 199 valid aggregated time-window samples. The system environment and algorithm parameters are detailed in Table 1.

Feature correlation and dataset composition
To assess the redundancy and complementarity of the sensor channels prior to modeling, a feature correlation analysis was performed. As illustrated in the P.......

Discussion

This protocol presents a robust methodology for motor condition identification in cold-start scenarios where labeled data is absent. By integrating unsupervised structure learning with pseudo-supervised classification, the method overcomes the limitations of traditional supervised learning, which relies heavily on human annotation^13,18,19. A critical step in this protocol is the selection of the number of clusters (K). .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Category			Component and version
Operating system			Windows 11 Professional 23H2
Processor			Intel Core i9-14900HX
Memory			32 GB
Graphics processor			NVIDIA GeForce RTX 4070 Laptop GPU (8 GB)
Python			3.12
NumPy			1.26
pandas			2.2
scikit-learn			1.5
Matplotlib			3.8
Seaborn			0.13
Random seed