What this page solves – blade load & SHM scope

Utility-scale onshore and offshore wind turbines increasingly rely on very long blades, where small deviations between design and real operating conditions can consume a large portion of fatigue life. This page focuses on the electrical and electronic design of blade-level load and structural health monitoring (SHM) nodes that turn raw strain, vibration and temperature signals on the blade into actionable load envelopes, damage indicators and events that can be consumed by nacelle controllers and SCADA systems.

Typical scenarios include offshore blades above 80 m, where extreme gusts, wind shear and yaw misalignment can push flapwise and edgewise bending close to design limits; winter sites, where asymmetric ice accretion on a single blade creates rotor imbalance and excessive loads; and cases where early cracks or delamination are not detected during visual or scheduled inspections and only become evident through abnormal deformation or modal behaviour. In all these cases, blade SHM nodes offer continuous, quantitative measurement of how hard each blade is being driven in the field, rather than relying only on design assumptions and sparse inspections.

At the mechanical interface, the blade SHM layer sits directly above the sensors embedded or bonded into the blade structure – resistive strain gauges, fibre Bragg grating (FBG) chains, MEMS accelerometers and local temperature sensors. At the electrical level, this page covers the analogue front-ends (AFEs), low-noise ADCs and edge processing that convert these raw signals into blade loads, fatigue indicators and event flags. At the system interface, blade SHM nodes stream compact metrics and events towards the hub aggregator, nacelle controller and SCADA or condition monitoring system, where control and maintenance decisions are taken.

The scope is intentionally limited to the blade-level sensing and electronics. This page does not define full turbine control strategies such as how pitch and yaw are scheduled over wind speed or how safety chains are implemented – those belong to dedicated pitch/yaw control and safety-chain topics. It also does not attempt to replace nacelle or tower vibration monitoring, drivetrain health monitoring or general environment sensing, which are described in hub and nacelle sensing pages. Lightning current paths, tower earthing and high-voltage insulation diagnostics are treated in lightning and surge monitoring topics; those pages explain how strike events are detected, while this page focuses on how post-strike stiffness or load signatures can be observed on the blade.

For system-level integration of blade SHM into turbine controllers and fleet analytics, reference content on nacelle controller and SCADA gateways. For details on pitch and yaw safety chains reacting to blade overload or ice accretion events, refer to the pitch/yaw safety topic. For complementary sensing on hub bearings, drivetrain and tower structures, refer to hub sensing and nacelle/tower environment monitoring pages. Together, these topics form a coordinated monitoring stack; this page is the blade-focused building block that defines what the blade node must measure, process and report.

Blade loads, damage mechanisms & SHM objectives

Utility-scale blades are subject to a combination of flapwise and edgewise bending, torsion and axial tension or compression. Flapwise bending is driven mainly by aerodynamic loads, while edgewise bending reflects gravity, rotor imbalance and yaw misalignment. Torsion appears when pitch settings deviate from their intended values or when inflow is highly non-uniform across the rotor. Axial forces arise from centrifugal loading and global pressure, and are concentrated near the root where geometry and stiffness change rapidly.

These load components vary strongly with operating condition. Under normal partial-load and rated operation, the blade experiences many millions of small to medium stress cycles that accumulate fatigue damage over years of service. Extreme gusts, wind shear events, emergency stops, grid faults and incorrect pitch settings can drive loads close to the design envelope for ultimate strength. Cold-climate sites add additional stress when asymmetric ice accretion on one or more blades creates rotor imbalance and elevated edgewise bending. Each of these load cases leaves a different signature in strain and vibration responses along the blade.

At the material level, these loads can cause fatigue cracking in laminates, adhesive joints and interfaces; local stiffness loss through delamination or debonding; and, after lightning or impact events, hidden damage that may not be visible from the outside. Lightning current paths, surge levels and electrical protection are addressed in dedicated lightning and surge monitoring topics; for blade SHM the important question is whether local stiffness and modal behaviour have changed compared to the healthy baseline. Small but persistent shifts in modal frequencies or strain distribution can indicate the early stages of such damage.

Blade load and SHM design therefore pursues three linked objectives. The first is real-time monitoring against a defined load envelope so that overload conditions and high-cycle excursions are detected promptly. The second is robust estimation of accumulated fatigue through long-term strain and load histories, enabling rainflow-based counting and damage equivalent load calculations at the edge node or higher in the SCADA stack. The third is reliable event logging for overloads, ice accretion, imbalance and suspected post-strike damage, with clear time-stamps and basic descriptors. Later sections translate these objectives into concrete requirements on sensors, analogue front-ends, ADCs and edge processing so that each blade node can provide trustworthy inputs to nacelle controllers and fleet analytics.

Sensor technologies & placement for blade load & SHM

Blade load and structural health monitoring starts with selecting and placing sensors that are sensitive to the deformation and motion that matter most. Two primary sensor families are used to capture structural response directly: resistive strain-gauge bridges and fibre Bragg grating (FBG) chains. Strain-gauge bridges bonded to the laminate or spar caps provide a direct view of flapwise and edgewise bending at specific locations. FBG fibres can host multiple sensing points on a single optical strand and offer attractive immunity to electromagnetic interference and lightning-induced disturbances, at the cost of more complex and expensive interrogation hardware.

Resistive strain gauges remain a common choice where cost and integration simplicity are critical or where existing bridge AFEs are already qualified. They interface directly to bridge conditioners and instrumentation amplifiers, and can be combined into half- or full-bridge configurations that include temperature-compensation arms. However, long copper runs inside the blade behave as antennas in a harsh lightning and EMI environment and place demanding requirements on shielding, common-mode rejection and surge protection. FBG solutions shift most of this burden into the optical domain: multiple gratings along the blade can be read through a single fibre with high resolution and good multiplexing, while the electrical interface is concentrated in the interrogation unit connected to the SHM node or hub.

Inertial and acceleration sensing complements strain-based measurements by tracking blade vibration and modal behaviour. One- and three-axis MEMS accelerometers are widely used to capture the dynamic response of the blade at the root, mid-span and tip. Their signals can be analysed to extract dominant modal frequencies, mode shapes and damping, and to detect changes associated with stiffness loss or damage. The same sensors can reveal ice accretion and de-icing events through changes in vibration patterns and distinct impact signatures when ice sheds or surface material is lost from the blade.

Environmental measurements complete the picture. Local temperature readings support compensation of strain gauges, FBGs and material properties, and also highlight abnormal hot spots or thermal gradients inside the blade. Humidity or moisture indicators can point to long-term ingress and degradation in regions that are otherwise difficult to inspect. These quantities usually do not require high bandwidth, so they can be sampled sparsely to conserve energy while still providing valuable context for interpreting strain and vibration trends over the life of the turbine.

Sensor placement along the blade must reflect both the load paths and practical integration constraints. The root region concentrates bending moments and axial forces and is a natural location for strain measurement linked directly to design load envelopes. Mid-span positions are important for observing global mode shapes and stiffness changes, while tip locations are especially sensitive to ice accretion, imbalance and higher-order modes. Some designs concentrate sensors near the root and route all signals to a single node; others deploy multiple nodes or distributed FBG chains along the blade length to enable more precise localisation of damage. Turbine-level vibration sensors on the hub, drivetrain and tower are handled in dedicated hub sensing and nacelle or tower monitoring topics; this section focuses exclusively on sensors embedded in or bonded to the blade itself.

AFEs for strain, FBG and acceleration – low-noise front-ends

The analogue front-end is the interface between delicate blade sensors and the digital world where load envelopes, fatigue metrics and events are calculated. For resistive strain-gauge bridges, the AFE must provide stable excitation, high common-mode rejection and sufficient programmable gain to resolve microstrain-level signals without saturating under extreme loads. Constant-voltage excitation is widely used and places tight requirements on supply noise and temperature drift, while constant-current schemes can improve behaviour in some configurations at the cost of higher circuit complexity and power consumption.

Bridge conditioning stages need both gain programmability and offset control. Residual strain from manufacturing, adhesive creep and gauge tolerances make perfect balance unrealistic, so the AFE typically includes hardware or digital offset trimming to centre the operating range. Instrumentation amplifiers and precision PGAs must deliver high CMRR to suppress common-mode disturbances on long cable runs, low input noise and low drift so that genuine changes in strain from loading and damage are not obscured. Input filtering and surge protection components are essential where metre-scale conductors are routed through a lightning-exposed structure; detailed surge ratings, protection device selection and test levels are addressed in lightning and surge monitoring topics, while this page focuses on maintaining measurement integrity at the AFE input.

FBG and other fibre-based sensing solutions concentrate complexity in the optical interrogation unit. From the blade SHM node perspective, the interface may appear as one or more analogue outputs that represent wavelength or derived strain, or as a digital data stream over SPI, LVDS or Ethernet. Analogue outputs require precision ADC channels with adequate resolution and linearity to resolve small wavelength shifts. Digital outputs move the burden to interface bandwidth, buffering and timing: the SHM node must be able to collect and time-stamp data from multiple gratings without overruns, and must align FBG readings with strain and acceleration channels when multi-sensor fusion is required.

Acceleration AFEs differ depending on whether legacy IEPE sensors or modern MEMS devices are used. IEPE sensors require a constant-current source and AC-coupled readout and are more common in drivetrain and nacelle applications. Blade SHM nodes more often use MEMS accelerometers with analogue or digital outputs. For analogue output MEMS devices, the AFE must implement anti-alias filtering matched to the modal frequency range of interest and feed a low-noise ADC with suitable dynamic range. Sigma-delta converters are attractive for narrowband, high-resolution vibration analysis, while SAR converters with simultaneous sampling support transient and multi-channel capture. Digital MEMS devices shift the focus to bus loading, synchronisation and timestamping rather than analogue noise and drift.

Across all sensor types, multi-channel synchronisation is a central requirement. Accurate reconstruction of mode shapes, transfer functions and damage indicators depends on strain and acceleration channels being sampled with well-defined relative timing and gain matching. This drives the choice of ADC architectures, clocking schemes and, where needed, PLLs or synchronisation interfaces back to the hub or nacelle controller. Later sections build on these AFE considerations to define system-level timing, integration with edge processing and reporting into the turbine control and SCADA stack.

Edge processing, SHM indicators & event logic

Blade SHM nodes must strike a balance between very simple threshold monitoring and complex health assessment. At the lightest end, the edge device only performs basic filtering and envelope checks on strain and acceleration signals, raising flags when instantaneous or short-term metrics cross predefined limits. Deeper implementations compute compact structural health indicators locally, reducing the need to stream raw data and enabling quicker decisions higher in the turbine and SCADA stack. The appropriate depth depends on available compute, power and communication bandwidth as well as on certification and validation constraints.

Several signal-processing functions are particularly well suited to blade edge nodes. Time-domain pre-processing includes removal of DC offsets, anti-alias and smoothing filters, and calculation of windowed RMS, peak and peak-to-peak values. In the frequency domain, short-window FFT or targeted algorithms such as Goertzel can track a small number of key modal frequencies and band-limited energy metrics. These indicators support detection of stiffness changes, imbalance and ice accretion without exporting full waveforms. For fatigue, the node may implement simplified cycle counting or accumulation of stress-range histograms and damage equivalent load values over coarse time intervals, leaving full rainflow analysis and life consumption estimation to higher-level analytics if required.

Event logic on the blade combines these indicators with time windows and persistence criteria. Typical event classes include overloads where bending or strain metrics exceed design envelopes, abnormal vibration patterns and mode-shape shifts, ice accretion and imbalance signatures, and a range of sensor or channel faults such as open circuits, short circuits or saturated readings. Each event type relies on one or more indicators and associated thresholds, with separate thresholds for raising and clearing conditions to introduce hysteresis and reduce chattering near the boundary of normal operation.

Time-over-threshold rules and debouncing are essential to avoid flooding the controller with transient or spurious alarms. Overload and fault events typically require only short persistence times, while suspected ice or stiffness change events are confirmed over much longer windows. The blade SHM node ultimately outputs structured events and status words, not full control decisions. Pitch and yaw safety chains, turbine controllers and farm-level systems are responsible for combining these blade events with hub, nacelle, tower and grid information to decide on derating, shutdown or emergency actions; those strategies are described in dedicated safety-chain topics.

Power & energy harvesting for blade SHM nodes

Electrical power inside a rotating blade is difficult and expensive to deliver, so local supply for SHM nodes must be treated as a constrained resource. Pulling additional power through new cabling and slip rings increases mechanical complexity and introduces additional failure points. In many retrofit and new-build projects, blade nodes therefore rely on on-blade energy sources such as batteries, supercapacitors and energy harvesting, with only limited or no continuous power provided from the hub or nacelle.

Practical architectures often combine a long-life primary or rechargeable battery with a supercapacitor and one or more harvesting inputs. The battery provides the baseline energy budget over years of operation, while the supercapacitor absorbs short-term peaks from high-rate sampling bursts or wireless transmissions. Vibration harvesters, inductive or magnetic couplers near the root and small photovoltaic patches on suitable blade surfaces can extend operating life and reduce battery depth-of-discharge. An energy-harvesting PMIC coordinates these sources, managing cold-start behaviour, voltage ramp-up, storage element protection and distribution of regulated rails to AFEs, ADCs, processors and radios.

The PMIC must handle very low and highly variable input power levels, especially from vibration and weak-light solar sources. Cold-start capability at low input voltage enables recovery after extended periods of low wind or darkness. Once sufficient energy has been accumulated, the device controls charging of the supercapacitor and battery, enforces voltage and temperature limits and performs simple maximum-power or optimum operating point tracking to keep harvesters in efficient regions. On the load side, it usually generates one or more supply rails and may implement load-priority schemes that shed non-critical functions when available energy falls below thresholds.

A disciplined power budget is essential to align node functionality with available energy. Sensors, AFEs, ADCs, processors and communication links all contribute. High-resolution ADCs and radio transmissions tend to dominate active power, while MCU sleep currents and quiescent bias currents become important over months and years. Duty-cycling strategies therefore play a central role: measurement windows and indicator updates can be scheduled more frequently during demanding wind conditions and less frequently in benign or idle periods, and low-power monitors can trigger wake-ups when potential events are detected. Deep-sleep modes, where only a real-time clock and minimal supervision remain active, support long endurance during outages and low-wind phases. These choices are specific to blade SHM, where long service life, centrifugal forces, temperature extremes and difficult access make reliable, low-maintenance power design critical, but do not require a full general-purpose energy-harvesting treatise.

Communications & data integrity – LPWAN, satellite and gateways

Blade SHM data must cross several boundaries before it becomes useful at turbine and farm level. The first hop is between each blade node and a hub or nacelle node, which operates in a rotating, lightning-exposed mechanical environment. This link may be wired, using field buses such as CAN or RS-485 or fibre runs through slip rings and rotary joints, or wireless, using short-range sub-GHz or proprietary RF. Wired links benefit from mature industrial protocols and noise immunity but require carefully designed cable routing, bend-radius control and robust slip rings. Wireless links avoid new cabling inside the blade and are attractive for retrofits, but must contend with blade shadowing, multipath fading and tight power budgets.

The second hop connects the hub or nacelle to ground-level infrastructure and cloud services. In onshore and near-shore sites, LPWAN technologies such as private LoRa or LoRaWAN networks can provide low-power, long-range connectivity from nacelle gateways to a substation or central control room. Cellular options such as NB-IoT, LTE-M or 4G/5G can then bridge from the site to the cloud. For remote offshore wind farms with limited terrestrial coverage, satellite backhaul offers global reach at the expense of higher latency and constrained bandwidth. In these cases, the reporting strategy must be designed for “event-first, trend-second” behaviour: blade nodes and nacelle gateways prioritise compact event logs and aggregated health metrics over continuous waveform data.

Data integrity and time information are key to making SHM results trustworthy across multiple blades and turbines. At the blade node, every measurement window and event is tagged with a local timestamp derived from a low-power RTC. The nacelle controller or gateway periodically pushes time corrections, aligning blade time bases to a common reference without requiring each node to implement the full time-synchronisation stack. Packet-level integrity is enforced through checksums or CRCs and sequence numbers, which allow the hub to detect lost, duplicated or out-of-order frames and apply selective retransmission where bandwidth permits. Store-and-forward buffers in the hub or nacelle absorb WAN outages and reinforce end-to-end delivery for critical SHM events.

For high-value assets and long service lifetimes, SHM data must also be tamper-resistant. Blade or nacelle nodes may therefore incorporate secure elements or hardware security modules to protect keys and sign event logs or health reports, providing verifiable provenance of overload and damage evidence. Likewise, configuration updates, threshold changes and firmware images should be authenticated and, where appropriate, encrypted before being applied to SHM nodes. The details of time synchronisation protocols, gateway security architecture and SCADA data models are covered in nacelle controller and SCADA gateway topics; this section focuses on the communication patterns and integrity measures specific to blade SHM in low-bandwidth, high-latency environments.

System integration with nacelle controller & SCADA

From a system perspective, a blade SHM module is a provider of structured information rather than an isolated sensor box. It exposes a small set of real-time load channels, higher-level health indicators and a well-defined event queue to the nacelle controller and SCADA environment. Real-time channels typically represent blade-root bending moments or equivalent strain values and selected acceleration or modal indicators, sampled at a rate consistent with control-loop needs. These measurements can be used directly in individual blade pitch algorithms, tower-top load mitigation and other turbine-level control strategies, without flooding the controller with raw waveforms from every sensor.

Health indicators abstract the results of long-term SHM processing into a form suitable for asset management and fleet-level dashboards. Examples include normalised health indices for each blade, accumulated damage or damage-equivalent load metrics and simple remaining life bands that group blades into broad categories such as “within design envelope”, “approaching fatigue limits” or “inspection recommended”. These indicators are updated on much slower timescales than real-time channels and are read periodically by SCADA systems or higher-level platforms. They complement instantaneous measurements by expressing what the blade has experienced over months and years rather than just the current operating point.

The event queue captures discrete occurrences such as overloads, high-cycle episodes near the design envelope, suspected ice or imbalance conditions, vibration anomalies and sensor or node faults. Each event is tagged with timestamps, blade identifiers, severity levels and a brief set of context values. Nacelle controllers and SCADA gateways ingest these events into alarm systems, historical incident logs and, where relevant, control decision logic. Integration at this level is typically object- or point-based and can be mapped onto a range of protocols such as fieldbuses, industrial Ethernet or SCADA-centric models; the exact mapping is handled by nacelle controller and gateway designs rather than by the blade SHM node itself.

Blade SHM modules interact closely but cleanly with pitch and yaw control systems. They supply hard constraints, such as load-limit reached or high ice severity, which should be treated as non-negotiable boundaries in control logic, and soft recommendations, such as preferred derating in certain wind conditions when fatigue margins are tight. Final decisions on derating, shutdown and emergency actions remain with nacelle and farm controllers and their certified safety chains. At the architectural level, the signal path can be viewed as a chain from sensors to blade node AFE and edge processing, to a hub aggregator, to the nacelle controller and finally into SCADA and cloud environments, with configuration, time and software updates propagating back down the same hierarchy.

Design checklist & IC role mapping for blade SHM nodes

Use this section as a structured checklist when reviewing a blade SHM node design. Each item connects back to earlier sections on sensors, AFEs, power, communications and environment. The goal is to confirm that the chosen architecture, components and margins are consistent with blade loads, fatigue and offshore conditions before locking down the hardware.

Sensor selection checklist – strain, FBG and acceleration

Confirm that the sensing concept matches the monitoring objectives and that channels, ranges and placements are defined before AFE and power decisions are made.

• Define which physical quantities must be observed: root bending (flapwise/edgewise), mid-span or tip loading, local strains, vibration and ice or imbalance indicators.
• Decide the mix of sensor technologies: electrical strain gauges vs FBG, single-point vs multiplexed fibres, 1-axis vs 3-axis accelerometers and the number of nodes per blade.
• Specify bandwidth per channel: slow load / fatigue monitoring (tens of hertz), modal tracking and ice detection (hundreds of hertz), and whether rare impact events require higher-bandwidth transient capture.
• Set target sampling rates for each class of channel and state whether channels must be synchronously sampled for modal analysis and load reconstruction.
• Translate resolution needs into engineering units: minimum useful µε for strain and µg or mg for acceleration, and maximum expected ranges under extreme wind, braking or pitch errors.
• Define ambient and internal temperature ranges and decide whether temperature channels are required for compensation and diagnostics, especially for FBG-based systems.

AFE & ADC checklist – noise, drift, protection and synchronisation

Front-end and converter performance determines whether blade SHM can resolve small changes in load and dynamics without being dominated by noise, drift or interference.

• Check that instrumentation amplifiers or bridge conditioners provide the required input noise density, gain range, CMRR and input bias for the chosen sensors.
• Confirm that ADC resolution and ENOB support the target strain and acceleration resolution with margin, taking into account total system noise and sensor factors.
• Verify offset and gain drift specifications for amplifiers, references and ADCs and plan any required auto-zero or periodic calibration sequences.
• Ensure that all channels participating in load reconstruction or modal analysis are synchronously sampled or share a well-defined clock and phase relationship.
• Design anti-alias filters with clear corner frequencies and verify that group delay is acceptable for the intended indicators and event logic.
• Define input protection for long sensor leads: series resistors, current limiting, TVS and RC networks compatible with the wider lightning and surge design of the turbine.
• Check supply rails and common-mode ranges: bridge excitation voltages, headroom for instrumentation amplifiers and ADC input compliance relative to expected sensor and cable drops.

Power & energy harvesting checklist

Power is often the tightest constraint inside a rotating blade. The design must ensure enough energy for decades of operation with realistic service intervals and environmental extremes.

• Estimate average and peak power consumption for sensors, AFEs, ADCs, processing and communication in all operating modes: normal wind, extreme events, low-wind and standstill.
• Select primary and secondary storage elements: battery chemistry, capacity and temperature rating, plus supercapacitor size for short bursts such as radio transmissions or high-rate logging.
• Identify energy sources available on the blade: vibration harvesters, inductive or magnetic coupling, small PV patches and any limited wired supply from the hub.
• Check that the energy-harvesting PMIC supports the required input types, cold-start voltage, minimum input power and charging strategy for the chosen storage elements.
• Define under-voltage thresholds and safe degradation modes: which functions are disabled first, and which minimum monitoring capabilities must be preserved under severe energy deficit.
• Include supervisory functions and watchdogs for both MCU and supply rails so that brown-out and firmware lockup result in controlled recovery, not silent failure.

Communications checklist – links, redundancy and loss handling

The communication plan must cover both blade-to-hub connectivity and onward transport to nacelle, SCADA and cloud, with clear strategies for latency, bandwidth and outages.

• Choose blade-to-hub links: confirm whether wired (CAN, RS-485, fibre) or wireless (sub-GHz RF) fits better with available slip rings, mechanical layout and retrofit constraints.
• Evaluate link budgets, antenna placement and fading for wireless options and define retransmission and acknowledgement policies compatible with power and latency limits.
• Define hub/nacelle-to-site connectivity: private LPWAN, Ethernet to substation or a combination, and align bandwidth budgets with SHM reporting strategies (event-driven vs continuous data).
• Plan wide-area backhaul: NB-IoT, LTE-M, 4G/5G or satellite, and specify what portion of SHM data is sent in real-time versus buffered and uploaded in batches.
• Specify redundancy and failover paths where required: local SCADA, maintenance channels and cloud connections, including health monitoring of each path.
• Define loss handling: where data are buffered (blade, hub, nacelle), how buffer overflow is prioritised and how missing or delayed data are flagged to higher-level analytics.

Mechanical & environmental checklist

The blade environment imposes mechanical, thermal and chemical stresses that must be reflected in component choice, packaging and mounting, not just in electrical ratings.

• Confirm acceleration and centrifugal load levels at the chosen mounting position and check that PCBs, connectors and housings meet relevant mechanical standards.
• Define the required enclosure protection level and sealing strategy, including coatings, partial or full potting and any venting to manage pressure and condensation.
• Assess humidity, moisture ingress and salt-fog exposure for offshore installations and select materials, coatings and connector systems accordingly.
• Review the relationship between the blade lightning system and SHM wiring and nodes: routing, shielding, bonding and separation from primary strike paths.
• Ensure that surge and transient protection on sensor, power and communication lines matches the overall lightning and surge concept for the turbine.

IC role mapping for blade SHM nodes

Once requirements are clear, semiconductor roles can be mapped to specific device classes. The following groups highlight typical IC functions and example part numbers; actual choices must be aligned with project standards, availability and certification needs.

Precision front-end & converter chain

Instrumentation amplifiers, bridge conditioners, PGAs and high-resolution ADCs form the measurement backbone for strain, FBG interfaces and acceleration.

• Instrumentation amplifiers and PGAs for strain and bridge sensors: low-noise, high-CMRR devices such as INA333, AD8421, MAX4208-class parts.
• Bridge/sensor front-ends with integrated ADC or reference: devices in the ADS124S08, AD7124-4, LTC2484 class.
• Multi-channel simultaneous-sampling ΣΔ ADCs for synchronous SHM: families similar to AD7768-4, ADS127L11 and related devices.

Power management, energy harvesting & storage monitoring

Dedicated PMICs coordinate energy harvesting, storage protection and regulated rails, while fuel gauges and supervisors track remaining energy and safe operation.

• Energy-harvesting PMICs with cold-start and ultra-low input power support: examples include BQ25570, LTC3331, AEM10941-class devices.
• Battery and supercapacitor chargers and power-path controllers: devices in the LTC4040, BQ2407x and similar families.
• Fuel-gauge ICs for state-of-charge and health estimation: MAX17055, BQ27421 and related parts.
• Voltage supervisors and reset generators: TPS3890, MAX16054-class devices and equivalents.

MCU and edge processing

Low-power microcontrollers implement sampling control, signal processing, event logic and communication stacks; edge-AI-capable variants enable more advanced SHM algorithms.

• Ultra-low-power Cortex-M MCUs with rich analog and timer peripherals: STM32L4/L5, MSP432, nRF52-class devices.
• MCUs and controllers with edge-AI or DSP acceleration where needed: STM32U5 with AI libraries, i.MX RT low-power variants and similar devices.

Communications – sub-GHz, LPWAN and cellular / satellite interfaces

Radio ICs and modules define the blade node link budget and power profile. Choice depends on blade-to-hub distance, farm topology and backhaul strategy.

• Sub-GHz RF transceivers for blade-to-hub links: devices in the SX1262, CC1312R, RF96x classes.
• LoRa/LoRaWAN and other LPWAN modules for hub/nacelle connectivity: examples include RAK3172, CMWX1ZZABZ-class modules.
• NB-IoT / LTE-M modules for wide-area backhaul: BG95, SIM7070 and similar devices.
• UART/SPI interfaces and power-management circuitry for satellite terminals where offshore coverage requires satellite backhaul.

Security & data integrity – secure elements and HSMs

Security devices protect identities and keys and enable integrity checks and signatures on SHM records, firmware and configuration updates where tamper evidence is required.

• Secure elements for key storage and signing: ATECC608A, SE050-class devices supporting ECC and hashing.
• MCUs with integrated security blocks or HSMs for secure boot and code protection, used where the SHM node must verify firmware and configuration authenticity.

Blade load & SHM – frequently asked questions

Use this FAQ as a quick decision guide when planning or reviewing blade SHM. Each question points back to the sections on loads, sensors, AFEs, edge processing, power, communications and integration so that you can dive deeper once you know which issue matters most in your project.