Digital fault diagnosis and full-lifecycle health management serve as core technical approaches to realize intelligent operation, predictive maintenance and long-term reliability improvement for high‑voltage power supplies. They fundamentally resolve persistent pain points such as difficult fault localization, high maintenance costs and heavy losses caused by unplanned downtime. By monitoring full-dimensional operating parameters in real time through digital control technologies, the system accurately identifies fault types, pinpoints failure locations, evaluates health status and remaining useful life, issues early warnings for potential defects, and enables predictive maintenance. This greatly enhances availability, safety and service life, covering high‑voltage power supply applications across industrial, power grid, medical, new energy and other sectors. Eight core technical challenges exist in digital fault diagnosis and health management for high‑voltage power supplies.
First, precise extraction of subtle fault features. High‑voltage power supplies suffer diverse failures including power device breakdown, capacitor aging, transformer defects, drive circuit faults, sampling loop anomalies and control loop failures. Fault signatures are typically weak, heavily coupled with electromagnetic interference and operational fluctuations, requiring algorithms capable of extracting valid features under strong noise and wide load variations. Second, accurate diagnosis and localization of coupled multiple faults. Failures often occur in cascaded modes, where one defect triggers secondary malfunctions. Traditional threshold alarms only detect severe faults and cannot identify early anomalies. Decoupled intelligent diagnosis is required to achieve fault classification and localization with accuracy ≥95%. Third, early weak fault detection and warning. Component aging and performance degradation progress slowly with extremely faint early signatures undetectable by conventional methods until catastrophic failures cause shutdowns. Health management algorithms must capture subtle drifting features to enable advance warnings and predictive maintenance. Fourth, reliable health evaluation across all operating conditions. Wide variations in input voltage, load current and ambient temperature induce normal parameter fluctuations that are easily mistaken for faults, causing false or missed alarms. Adaptive health assessment models must distinguish operational drift from genuine degradation under all working scenarios. Fifth, accurate remaining useful life prediction. Service life is affected by electrical stress, temperature, switching cycles and dynamic operating conditions. Traditional empirical estimation yields large errors; data-driven aging models are needed to predict remaining life with error ≤10%. Sixth, high real-time performance with low computing overhead. Fault protection requires nanosecond-to-microsecond response while embedded DSP/FPGA resources are limited. Diagnosis algorithms must be lightweight, high-speed and non-intrusive to critical control loops. Seventh, fault data accumulation and model generalization. Real-world fault samples are scarce, and feature distributions differ greatly across topologies and product models. Self-learning adaptive models are essential to continuously optimize diagnosis performance and support rapid migration to new products. Eighth, operational compatibility and full data traceability. Many high‑voltage systems operate in remote, harsh or high-risk environments, demanding remote monitoring, intelligent guidance and tamper-proof lifelong data logging to comply with full-lifecycle asset management requirements.
Addressing these challenges, the methodology establishes a complete full-lifecycle framework: real‑time condition monitoring → fault feature extraction → intelligent diagnosis → health evaluation → remaining life prediction → predictive maintenance. It enables early warning, precise localization and accurate lifetime estimation, breaking traditional bottlenecks in difficult troubleshooting, costly maintenance and frequent unplanned outages. The design follows eight core principles. First, full-dimensional real-time monitoring covers power loops, control circuits, drive units and environmental parameters. More than 30 key metrics are sampled via high-precision ADCs across DC to MHz bandwidth, including input/output voltage/current, junction temperature, transformer temperature, drive voltage ripple and thermal data. High-speed fault waveform recording at ≥10 MSPS automatically captures 100 ms pre- and post-failure transient data to support detailed analysis. Second, multi-stage feature extraction adopts digital filtering, operating-condition normalization and time-frequency analysis. Kalman filtering and wavelet denoising suppress interference; condition normalization eliminates load/temperature drift; time-domain, frequency-domain and wavelet entropy analysis extract over 20 standardized fault indicators for reliable model input. Third, intelligent decoupled fault diagnosis builds a comprehensive fault signature library covering open/short power devices, capacitance degradation, transformer insulation aging, reference drift and fan failures. Machine learning classifiers based on SVM, random forests and neural networks achieve multi-fault decoupling with diagnosis accuracy ≥95%. Rule-based expert systems automatically generate troubleshooting guidelines and maintenance workflows. Fourth, early anomaly detection integrates Gaussian mixture models and isolation forest algorithms to establish normal-state baseline profiles. Minor deviations invisible to threshold alarms are detected 3–6 months in advance. Fault severity grading (mild, moderate, critical, emergency) prevents false alarms and enables graded response strategies. Fifth, full-condition adaptive health assessment adopts fuzzy comprehensive evaluation with weighted health indicators including on-resistance drift, capacitance decay, stability, ripple and temperature rise. Analytic hierarchy processing determines indicator weights, delivering a clear 0–100 health score for intuitive asset evaluation under all operating modes. Sixth, precise remaining life prediction implements a three-layer aging framework based on physics-of-failure models for semiconductors, capacitors, transformers and cooling components. Real-time electrical and thermal stress calculation combined with rainflow counting and Miner cumulative damage theory estimates component-level and system-level lifetime with prediction error ≤10%. Historical trend analysis optimizes maintenance scheduling. Seventh, low-latency lightweight deployment distributes tasks across FPGA and DSP. FPGA handles nanosecond hardware protection, high-speed sampling and feature extraction with response ≤1 μs for critical failures. DSP executes diagnosis, health scoring and lifetime prediction with lightweight fixed-point algorithms to ensure stable main control loop performance without resource conflict. Eighth, full-lifecycle traceable operation integrates Ethernet, 4G/5G and optical fiber communication with standard protocols including Modbus, MQTT and OPC UA for seamless industrial IoT connectivity. Encrypted non-volatile storage retains over 10 years of operating logs, fault records, calibration data and maintenance history with full anti-tamper traceability. The system automatically generates maintenance schedules and spare-part recommendations based on real-time health and lifetime data, maximizing equipment availability and minimizing long-term operational costs.