Industrial Electronics

Industrial electronics encompasses the design, development and deployment of electronic systems that drive modern manufacturing, energy conversion, transport and automation. At its core lie power‐conversion circuits—such as DC–DC converters, AC–DC rectifiers and inverters—that employ high‐frequency switching to achieve efficiencies approaching unity. Drive electronics for induction and permanent‐magnet machines integrate advanced control algorithms, sensor feedback and wide‐bandgap semiconductors to deliver precise torque, speed regulation and enhanced dynamic response. Condition‐monitoring subsystems fuse vibration, current, temperature and magnetic‐flux sensing with signal‐processing techniques to identify incipient faults and support predictive maintenance. Recent advances harness machine learning and edge computing to analyse real‐time data streams on low‐cost hardware, reducing latency and dependence on cloud services. Networked architectures, compliant with Industry 4.0 standards, enable interoperable diagnostics and remote configuration, while digital‐twin models drive virtual commissioning and lifecycle optimisation. As industrial electronics evolves, it underpins efforts to reduce energy consumption, improve system reliability and accelerate the transition to smart, sustainable manufacturing.

Research from Nature Portfolio

A novel relay algorithm based on cross‐ and auto‐coherence has been demonstrated for protection of AC‐machine stator windings. By analysing the frequency‐domain correlation between differential currents at the neutral and supply terminals, this method distinguishes interturn and shunt faults with over 98 % reliability and sub-millisecond tripping in live testing. In parallel, thermography‐assisted fault diagnosis has been elevated by integrating a modified InceptionV3 deep‐learning model with contrast‐limited adaptive histogram equalisation and a squeeze-and-excitation channel‐attention module. Trained on hundreds of high-resolution thermal images covering a broad fault taxonomy, the approach achieves near-99 % classification accuracy, illustrating the power of combining imaging and advanced neural architectures for non-invasive motor health assessment.

Research from all publishers

Edge‐based intelligence has been applied to motor‐current signature analysis, deploying feature-extraction and classification pipelines on microcontroller platforms. By executing machine‐learning models locally—trained to recognise broken rotor bars and load anomalies—small and medium enterprises can achieve real-time fault detection without cloud connectivity, improving uptime and safety. In motion control, a cascade one-proportional-derivative controller with embedded filter, optimised by a bio-inspired snake algorithm, has been implemented on digital signal processors for brushless DC motors. This auto-tuned scheme mitigates integral wind-up and derivative chattering, halves transient settling times and enhances disturbance rejection compared with conventional PID and standalone 1PDf regulators.

Industrial Electronics publication trend

The graph below shows the total number of articles in industrial electronics across all publications each year (not limited to Nature Index journals).

Technical terms

Coherence technique: A frequency‐domain measure of similarity between two signals used to detect and classify faults in electrical windings.

Differential current relay: A protection device that compares currents at two points to identify leakage or internal winding faults.

Infrared thermography: Non‐intrusive imaging of surface temperature distributions to reveal subsurface electrical or mechanical anomalies.

Pulse‐width modulation (PWM): A switching strategy that controls average voltage by varying the ratio of on-time to total period.

Duty cycle: The fraction of each PWM period during which the switch is closed, governing effective output amplitude.

Edge computing: Localised data processing near sensors or devices to reduce latency and minimise cloud dependency.

Convolutional neural network (CNN): A deep‐learning architecture that applies layered convolutional filters to extract hierarchical features from spatial data.

References

1. Experimental performance examination of a coherence technique-based numerical differential current relay for AC machine stator windings protection. Scientific Reports (2025).
2. A deep learning approach for electric motor fault diagnosis based on modified InceptionV3. Scientific Reports (2024).
3. The Edge Application of Machine Learning Techniques for Fault Diagnosis in Electrical Machines. Sensors (2023).
4. Effective speed control of brushless DC motor using cascade 1PDf-PI controller tuned by snake optimizer. Neural Computing and Applications (2024).