Intelligent Design, Diagnostics and Digital Transformation

Introduction and Context

Power-electronic systems have evolved from locally controlled converter circuits into cyber-physical energy systems. A modern converter may contain wide-bandgap semiconductor switches, high-speed sensing, embedded processors, communication interfaces, cloud connectivity, digital twins and machine-learning-assisted maintenance. Consequently, design quality is no longer determined only by rated voltage, current, efficiency and thermal margin. It also depends on observability, diagnosability, cybersecurity, data quality, software assurance, lifecycle cost and the ability to adapt to changing operating conditions.

Intelligent design means embedding computation, sensing and decision-making into the engineering process and, where justified, into the converter itself. Diagnostics means identifying abnormal behaviour, locating its source and estimating its severity before failure occurs. Digital transformation is the organisational and technical transition from isolated, document-based engineering to connected, model-based, data-supported lifecycle management.

These ideas are particularly important in renewable-energy converters, electric-vehicle chargers, aircraft power systems, data-centre supplies, solid-state transformers and microgrids. In such applications, failure can interrupt critical services, damage expensive equipment or create safety hazards.

Learning Objectives

After studying this chapter, the reader should be able to:

1. Explain intelligent design, condition monitoring, fault diagnosis and digital transformation in power electronics.
2. Construct a requirements-to-validation workflow for a digitally enabled converter.
3. Apply reliability, thermal and diagnostic metrics to design decisions.
4. Distinguish model-based, signal-based and data-driven diagnostic methods.
5. Design an appropriate sensing, communication and cybersecurity architecture.
6. Evaluate the limitations, risks and governance requirements of artificial intelligence in power-electronic systems.
7. Develop a technically justified maintenance and lifecycle strategy.

1. From Converter Design to Intelligent System Design

Conventional design begins with an electrical specification: input range, output voltage, power rating, switching frequency, efficiency and transient response. These remain essential, but an intelligent design process adds the following questions:

• What variables must be measured to detect degradation?
• Which faults are safety-critical and how quickly must they be detected?
• Can the controller distinguish a sensor fault from a power-stage fault?
• What data should be stored locally, transmitted remotely or discarded?
• How will software updates, authentication and access control be managed?
• Can the system continue safely when a sensor or communication link fails?

A useful abstraction is the closed lifecycle:

requirements → physics-based design → digital model → prototype → sensing and control → verification → operation → diagnostics → feedback to redesign

The central principle is that diagnosability should be designed, not added after commissioning. A converter with excellent efficiency but poor fault observability may have a lower practical value than a slightly less efficient converter whose remaining useful life can be estimated reliably.

1.1 Intelligent design dimensions

Intelligence in power electronics has several complementary dimensions:

Intelligence is not synonymous with artificial intelligence. A well-designed threshold detector, observer or redundant sensor arrangement can be more reliable and interpretable than a complex neural network.

2. Design for Reliability, Safety and Diagnosability

2.1 Reliability fundamentals

Reliability is the probability that a component or system performs its required function for a stated time under stated conditions. If a constant failure rate λ is assumed, the reliability function is:

where t is time and λ is the failure rate in failures per unit time. The corresponding mean time to failure under the constant-rate assumption is:

These equations apply only when the failure rate is approximately constant and failures are statistically independent according to the adopted model. Semiconductor power modules often experience an early-life period, a useful-life period and a wear-out period; therefore, a constant λ may be unsuitable for lifetime prediction.

For a series system, in which every element must operate, reliability is:

For statistically independent parallel redundant elements, where the system works if at least one element works:

Redundancy improves reliability only if the redundant elements, sensors and switching paths do not share a dominant common-cause failure.

2.2 Availability and maintainability

Availability concerns whether the system is operational when required. A simple steady-state approximation is:

where MTBF is mean time between failures and MTTR is mean time to repair. This expression assumes statistically stable repair and failure processes. Diagnostics can increase availability by reducing MTTR and enabling planned maintenance, even when they do not reduce the physical failure rate.

2.3 Reliability-oriented converter design

Important failure mechanisms include:

• thermal cycling and bond-wire fatigue;
• solder fatigue and substrate delamination;
• gate-driver malfunction;
• capacitor electrolyte evaporation, dielectric wear and equivalent-series-resistance increase;
• magnetic insulation breakdown and core overheating;
• connector degradation and vibration-induced intermittency;
• sensor drift and analogue-to-digital converter errors;
• software timing faults and communication loss.

A reliability-oriented design therefore combines electrical derating, thermal management, mechanical design, component qualification, fault containment and diagnostic coverage.

2.4 Diagnostic coverage

Diagnostic coverage expresses how effectively a diagnostic mechanism detects relevant failures. A simplified measure is:

where λ_detected and λ_undetected are the rates of dangerous failures detected and not detected by the diagnostic function. The equation is meaningful only after clearly defining the failure population, mission time, diagnostic test interval and dangerous-failure category. It should not be treated as a universal quality score.

3. Architecture of an Intelligent Power-Electronic System

A robust architecture normally contains five layers.

3.1 Physical power layer

This includes switches, diodes, capacitors, inductors, transformers, busbars, cooling systems and mechanical assemblies. Its design is governed by voltage stress, current stress, switching loss, conduction loss, insulation coordination, electromagnetic compatibility and thermal constraints.

3.2 Measurement layer

Sensors measure current, voltage, temperature, pressure, vibration, acoustic emission or magnetic fields. Sensor selection must consider bandwidth, isolation, accuracy, latency, noise, common-mode range, calibration stability and failure modes. A sensor should be selected not merely because it measures a variable, but because the measurement supports a specific control or diagnostic decision.

3.3 Control and edge-computing layer

A digital signal processor, microcontroller, FPGA or system-on-chip executes modulation, protection, estimation and diagnostic algorithms. Time-critical protection should remain local. Cloud connectivity must never be required for a response whose safe operating time is shorter than communication latency or whose operation would be unsafe during network loss.

3.4 Communication and data layer

Protocols may connect gate drivers, control boards, supervisory systems and enterprise asset-management platforms. Data architecture should define sampling rates, timestamps, units, quality flags, retention periods and ownership. Time synchronisation is important when correlating electrical and thermal events across multiple converters.

3.5 Application and governance layer

This layer supports fleet monitoring, maintenance planning, asset analytics, digital-twin updating and engineering change management. It also enforces authentication, authorisation, encryption, secure boot, signed firmware, logging and incident response.

4. Diagnostic Reasoning: From Measurement to Decision

A diagnostic system typically performs four functions:

1. Detection: determine whether behaviour differs from an acceptable reference.
2. Isolation: identify the subsystem or component responsible.
3. Identification: classify the fault type and operating severity.
4. Prognosis: estimate future degradation or remaining useful life.

A useful distinction is between a fault, a failure and an alarm. A fault is an abnormal condition; a failure is loss of a required function; an alarm is a communicated decision that a defined limit or rule has been met. These are not interchangeable.

4.1 Model-based diagnosis

A mathematical model predicts measurable behaviour. The difference between measured output and predicted output is a residual:

Here k is the sample index and both terms must refer to the same variable, units and time alignment. Under healthy operation, the residual should remain within a tolerance that accounts for modelling error, sensor noise and parameter variation.

A state observer can estimate internal variables that are not directly measured. In a linear discrete-time model:

where x is the state vector, u the input, y the measured output, and A, B, C and D system matrices. An observer generates an estimate x̂ and residuals from the difference between measured and estimated outputs. Observability is essential: if a fault changes no measured quantity, it cannot be isolated using that measurement set.

Model-based methods are interpretable and can detect faults without large historical datasets. Their weaknesses are sensitivity to parameter uncertainty, ageing and unmodelled nonlinearities.

4.2 Signal-based diagnosis

Signal-based methods extract indicators from measured waveforms. Common features include:

• mean, root-mean-square value and peak value;
• ripple amplitude and switching-frequency sidebands;
• harmonic magnitude and total harmonic distortion;
• temperature rise and thermal-cycle count;
• current imbalance among parallel devices;
• spectral energy, wavelet coefficients and kurtosis.

For a periodic voltage waveform, total harmonic distortion is commonly defined as:

where V₁ is the rms fundamental component and V_h is the rms component of harmonic order h. THD is dimensionless and is often reported as a percentage after multiplication by 100. The frequency range and measurement window must be specified.

Signal methods are often computationally efficient, but a feature may be affected by load, modulation index, grid conditions and sensor bandwidth. A feature should therefore be normalised or interpreted conditionally.

4.3 Data-driven diagnosis and machine learning

Supervised classification uses labelled examples of healthy and faulty behaviour. Unsupervised or semi-supervised methods learn the normal operating distribution and identify deviations. Regression models estimate quantities such as junction temperature or capacitor health.

A data-driven workflow should include:

1. Define the decision and its consequences.
2. Acquire representative data across load, temperature, grid and ageing conditions.
3. Remove leakage between training and test data; for time-series data, split by mission, asset or time period rather than randomly mixing adjacent samples.
4. Engineer or select physically meaningful features.
5. Train the model with class imbalance and uncertainty considered.
6. Validate on unseen operating conditions and, ideally, unseen hardware.
7. Set decision thresholds according to safety and maintenance costs.
8. Monitor drift after deployment.

Accuracy alone is inadequate. A missed dangerous fault may be much more costly than a false maintenance alert. Precision, recall, false-alarm rate, detection latency, calibration and robustness should be reported.

4.4 Sensor fusion

Sensor fusion combines partially informative measurements. For example, an increase in capacitor ESR may appear as greater bus-voltage ripple, increased capacitor temperature and altered transient response. The combination is stronger evidence than any single indicator. Fusion may be rule-based, probabilistic or learned, but the system should preserve sensor-quality flags and distinguish missing data from normal values.

5. Digital Twins and the Digital Thread

A digital twin is a computational representation of a physical asset that is linked to the asset through data over a defined lifecycle. It is more than a static simulation model. A useful twin may contain:

• topology, ratings and serialised component identity;
• electromagnetic, thermal and mechanical models;
• controller and firmware versions;
• measured operating history;
• parameter estimates and health indicators;
• maintenance records and engineering changes.

The digital thread is the traceable flow of information from requirements through design, manufacturing, commissioning, operation and retirement. Together, the twin and thread reduce inconsistency between engineering models and field reality.

A twin must state its fidelity and validity domain. A thermal model calibrated at 25 °C and nominal airflow should not automatically be trusted for blocked airflow, altitude or a modified cooling plate. Model updating should preserve version control, calibration records and uncertainty estimates.

6. Intelligent Design Methods

6.1 Multi-objective optimisation

Power-electronic design usually involves conflicting objectives: efficiency, mass, cost, volume, reliability, acoustic noise and control performance. A design is Pareto-optimal when no objective can be improved without worsening at least one other objective.

A weighted objective may be written as:

where f_i are normalised objective functions and w_i are non-negative weights. The result depends strongly on scaling and stakeholder priorities; therefore, a single weighted score should not conceal the Pareto trade-off.

Optimisation may use analytical design equations, finite-element models, circuit simulation, surrogate models or evolutionary algorithms. Surrogates reduce computational cost but must be validated in regions where decisions are made.

6.2 Digital control and adaptive operation

Digital controllers enable parameter scheduling, online estimation and predictive control. However, adaptive behaviour must be bounded. A controller should include saturation handling, watchdog supervision, fallback parameters and a safe state. A machine-learning policy should not directly command an unconstrained high-power converter without a verified safety envelope.

6.3 Design for testability

Design-for-testability provisions include test points, controllable operating modes, built-in self-test, sensor redundancy, timestamped event logs, known excitation sequences and accessible firmware diagnostics. These provisions can reduce commissioning time and improve fault isolation.

7. Cybersecurity, Safety and Governance

Connectivity creates attack surfaces. Threats include unauthorised firmware modification, manipulated sensor data, denial of service, credential theft, replay attacks and lateral movement from an industrial network. Cybersecurity must be treated as a safety concern because a malicious command can produce electrical overstress or loss of protection.

Good practice includes:

• threat modelling during system design;
• network segmentation and least-privilege access;
• secure boot and signed firmware;
• cryptographic key management;
• authenticated time and event records;
• vulnerability management and patch procedures;
• independent protection paths for critical trips;
• offline recovery and configuration backup;
• supplier and component traceability.

AI introduces additional governance questions. Engineers must know the training-data provenance, intended operating domain, uncertainty, update process and responsibility for decisions. An opaque model should not be allowed to suppress a hardware protection trip merely because it predicts normal operation. Safety functions require deterministic, verified mechanisms; AI may support supervision, prioritisation or maintenance planning.

Relevant compliance work may involve functional-safety processes, electromagnetic-compatibility requirements, grid codes, machinery or product safety rules, industrial cybersecurity standards and sector-specific regulations. Exact obligations depend on jurisdiction and application.

8. Worked Example: Diagnostic Threshold for DC-Link Ripple

Consider a 400 V converter whose measured dc-link ripple is 6 V rms under a defined operating condition. A diagnostic model predicts 4 V rms with a modelling uncertainty of ±0.8 V rms. Suppose the alarm threshold is set at 2.5 times the uncertainty above the prediction.

The residual is:

The threshold is:

Thus the observation is exactly at the nominal alarm boundary. It should not be interpreted as proof of capacitor failure. The decision should account for repeated observations, load condition, sensor uncertainty and the cost of a false alarm. A practical rule might require the threshold to be exceeded for several windows and might combine ripple evidence with capacitor temperature and estimated ESR.

This example illustrates three principles: residuals require a reference model, thresholds require uncertainty analysis, and a single alarm should initiate investigation rather than automatically identify a component.

9. Applied Case: Illustrative Fleet of Fast EV Chargers

Background

Consider an illustrative fleet of 150 kW dc fast chargers using an isolated ac–dc front end and a dc–dc output stage. Operators report intermittent derating and rising maintenance costs during summer operation.

Problem

The original monitoring system records input power, output voltage and cabinet temperature once per minute. It cannot distinguish blocked filters, cooling-fan degradation, sensor drift and semiconductor ageing. Unplanned service visits are frequent.

Analysis

An engineering team performs failure-mode and effects analysis and identifies fan degradation, dc-link capacitor ageing, gate-driver faults and connector heating as high-priority mechanisms. It instruments a pilot group with higher-rate current and voltage measurements, heatsink temperatures, fan speed, pressure drop and selected vibration channels. All data are timestamped and labelled with operating power, ambient temperature and firmware version.

A reduced-order thermal model estimates expected heatsink temperature. Residuals are combined with fan-speed information. Capacitor health is assessed using ripple, temperature and transient-response features. The team uses a semi-supervised anomaly detector trained mainly on verified healthy data, while retaining deterministic overtemperature and overcurrent trips.

Approach and solution

The architecture is divided into three decision levels:

1. Protection: hardware and local firmware trips for immediate hazards.
2. Maintenance alert: edge diagnostics identify persistent deviations and recommend inspection.
3. Fleet analytics: a central platform compares assets under comparable conditions and prioritises service routes.

The model is validated using chargers excluded from training. Thresholds are selected using the cost of missed failures, unnecessary visits and customer downtime. Firmware is signed, remote access is role-based and every diagnostic decision includes a confidence and data-quality flag.

Outcome

In the illustrative scenario, the pilot reduces unnecessary visits because fan blockage is identified before thermal derating. It also reveals that several apparent capacitor alarms were caused by a drifting voltage sensor. The principal benefit is not the anomaly algorithm alone; it is the combination of better instrumentation, operating-context data, deterministic protection and disciplined maintenance processes.

Learning points

• Diagnosis requires context; identical ripple values may have different meanings at different loads.
• Sensor faults must be considered explicitly.
• Edge protection and cloud analytics serve different time scales.
• Validation on unseen chargers is more credible than random splitting of closely related records.
• Digital transformation succeeds only when engineering, service and cybersecurity processes change together.

10. Challenges and Limitations

Data limitations

Fault data are rare, imbalanced and often poorly labelled. Data collected during commissioning may not represent ageing equipment. Changes in firmware, sensors or topology create distribution shift.

Model limitations

High-fidelity models can be computationally expensive; simplified models may omit the mechanism of interest. Machine-learning models can learn correlations caused by operating conditions rather than faults. Explainability tools do not guarantee causal validity.

False alarms and missed detections

A low threshold produces nuisance alarms and maintenance fatigue. A high threshold delays intervention. Thresholds should be selected through risk analysis, not solely through statistical convenience.

Integration cost

Additional sensors, processing, secure communications and data storage increase cost, power consumption and engineering complexity. The business case should compare these costs with avoided downtime, safety improvement, energy savings and extended asset life.

Human factors

Operators may distrust unexplained recommendations or over-trust automated outputs. Interfaces should display evidence, confidence, trend, recommended action and escalation path. Final responsibility must be assigned clearly.

11. Best-Practice Workflow

1. Define critical functions, hazards and failure consequences.
2. Perform failure-mode, effects and criticality analysis.
3. Identify measurable precursors and assess observability.
4. Select sensors with bandwidth, isolation and failure behaviour appropriate to the task.
5. Separate protection, control, diagnosis and fleet analytics by required response time.
6. Establish a data dictionary containing units, timestamps, quality flags and ownership.
7. Develop physics-based baselines before adopting complex AI.
8. Validate models across operating conditions, assets and time periods.
9. Quantify false alarms, missed detections, latency and uncertainty.
10. Provide fallback operation, watchdogs and safe states.
11. Secure the full lifecycle, including suppliers, firmware and remote maintenance.
12. Monitor model drift and revise the digital twin through controlled change management.

12. Contemporary Developments and Research Directions

Current research is examining physics-informed machine learning, federated learning across fleets, self-supervised learning from unlabelled waveforms, online parameter estimation, solid-state transformers, edge AI on gate-driver platforms and uncertainty-aware prognostics. Wide-bandgap devices create new diagnostic opportunities through faster switching signatures, but they also impose higher measurement bandwidth, electromagnetic-interference challenges and stricter layout requirements.

Research questions remain open. How should remaining useful life be defined when mission profiles change? How can diagnostic models transfer between different converter topologies? How can uncertainty be communicated to maintenance personnel? What evidence is sufficient to certify an adaptive algorithm? These questions require collaboration among power-electronics engineers, reliability specialists, data scientists, cybersecurity experts and safety assessors.

Chapter Summary

Intelligent power-electronic design integrates electrical, thermal, mechanical, computational and lifecycle considerations. Reliability, availability and diagnostic coverage provide complementary measures, but each depends on explicit assumptions and failure definitions. Model-based diagnosis uses residuals and observers; signal-based diagnosis extracts waveform and thermal features; data-driven methods classify or predict behaviour but require representative data, careful validation and drift management. Digital twins and digital threads connect design models, operational data, maintenance history and engineering changes. Cybersecurity, functional safety, explainability and human responsibility must be designed alongside algorithms. The most dependable architecture keeps fast protection local and deterministic while using edge and fleet analytics for supervision and maintenance. Successful digital transformation is therefore a socio-technical engineering programme, not merely the installation of an AI model.

Key Terms

Intelligent design: Design that integrates sensing, computation, optimisation and lifecycle decision-making.

Condition monitoring: Continuous or periodic observation of variables related to equipment health.

Fault: An abnormal condition that may or may not cause loss of function.

Failure: Inability to perform a required function.

Diagnostics: Detection, isolation and identification of abnormal conditions.

Prognostics: Estimation of future degradation or remaining useful life.

Reliability: Probability of performing a required function for a specified time and condition.

Availability: Probability that an asset is operational when required.

Diagnostic coverage: Proportion of defined dangerous failures detected by diagnostics.

Residual: Difference between measured and model-predicted behaviour.

Observer: An estimator of internal system states from inputs and outputs.

Sensor fusion: Combination of multiple measurements to improve inference.

Digital twin: A lifecycle-linked computational representation of a physical asset.

Digital thread: Traceable flow of information across the asset lifecycle.

Edge computing: Processing data near the physical equipment.

Model drift: Degradation of model performance as operating conditions or assets change.

False alarm: An alert issued when the suspected fault is not present.

Common-cause failure: Failure of supposedly independent elements due to a shared cause.

Secure boot: Startup process that executes only authenticated software.

Remaining useful life: Estimated time or usage before a defined failure criterion is reached.

Review Questions

Short Answer Questions

1. Distinguish fault, failure and alarm in a power-electronic system.
2. State the assumptions behind the constant-failure-rate reliability model.
3. What is a diagnostic residual?
4. Why should fast protection remain local to the converter?
5. Define a digital twin and distinguish it from a static simulation model.

Long Answer Questions

1. Explain how reliability, availability and diagnosability influence converter architecture.
2. Compare model-based, signal-based and data-driven diagnostic methods.
3. Discuss the role of sensor fusion in detecting capacitor and cooling-system degradation.
4. Explain how cybersecurity and functional safety interact in connected converters.
5. Describe a complete workflow for deploying a machine-learning diagnostic model in an industrial power-electronics fleet.

Analytical / Application Questions

1. A converter has MTBF of 50,000 h and MTTR of 10 h. Calculate its approximate steady-state availability using A = MTBF/(MTBF + MTTR), and discuss the assumptions.
2. A measured temperature is 12 °C above a model prediction, while model uncertainty is ±4 °C. Determine whether a 2.5-uncertainty alarm rule is met and explain why repeated observations may still be required.
3. Design a diagnostic architecture for a 100 kW bidirectional battery converter. Identify critical sensors, local protection functions, edge diagnostics, cloud-level analytics and at least three cybersecurity controls.

Multiple Choice Questions with Answers

Question 1

Which function determines whether abnormal behaviour differs from an acceptable reference?

A. Detection

B. Isolation

C. Identification

D. Prognosis

Correct Answer: A. Detection

Question 2

Under a constant failure-rate assumption, reliability is expressed as:

A. R(t) = 1 − e^(−λt)

B. R(t) = e^(−λt)

C. R(t) = λt

D. R(t) = t/λ

Correct Answer: B. R(t) = e^(−λt)

Question 3

A residual in model-based diagnosis is the difference between:

A. Input power and output power

B. Measured and model-predicted behaviour

C. Rated and switching frequency

D. MTBF and MTTR

Correct Answer: B. Measured and model-predicted behaviour

Question 4

Which method is most directly dependent on labelled healthy and faulty examples?

A. Supervised classification

B. Hardware overcurrent protection

C. Passive thermal conduction

D. Secure boot

Correct Answer: A. Supervised classification

Question 5

Why should a cloud connection not be required for an immediate overcurrent trip?

A. Cloud systems cannot store data

B. Communication latency or loss may compromise safe response

C. Overcurrent is unrelated to converter operation

D. Cloud systems always reduce efficiency

Correct Answer: B. Communication latency or loss may compromise safe response

Question 6

For a system with MTBF = 50,000 h and MTTR = 10 h, approximate availability is:

A. 0.5001

B. 0.9000

C. 0.9998

D. 1.5000

Correct Answer: C. 0.9998

Question 7

Which statement best describes a digital twin?

A. A static wiring diagram only

B. A lifecycle-linked computational representation of a physical asset

C. A replacement for all physical sensors

D. An unauthenticated firmware copy

Correct Answer: B. A lifecycle-linked computational representation of a physical asset

Question 8

A high false-alarm rate most directly tends to cause:

A. Maintenance fatigue and unnecessary interventions

B. Perfect fault isolation

C. Lower sensor noise

D. Guaranteed longer component life

Correct Answer: A. Maintenance fatigue and unnecessary interventions

Question 9

Which control is most appropriate for preventing unauthorised firmware modification?

A. Signed firmware and secure boot

B. Increasing switching frequency

C. Reducing heatsink size

D. Removing event logs

Correct Answer: A. Signed firmware and secure boot

Question 10

In the THD expression, V₁ represents the:

A. Rms fundamental component

B. Peak switching voltage only

C. Average dc-link voltage

D. Highest harmonic component

Correct Answer: A. Rms fundamental component