Designing a Deployable 10BASE-T1L Single-Pair Ethernet Condition Monitoring Vibration Sensor


For condition-based monitoring (CbM) sensor development, single-pair Ethernet (SPE), or 10BASE-T1L, offers significant advantages compared to standard Ethernet, including reduced sensor size, reduced complexity, and low cost cabling options. This article will discuss how to design a tiny, shared power and data interface (PoDL) for a CbM sensor. This article will also discuss power supply design, mechanical design, MEMS sensor selection, and software design for a complete sensor solution.


New single-pair Ethernet (SPE), or 10BASE-T1L, physical-layer standards developed by the IEEE are offering new connectivity solutions for communicating asset health insights for condition-based monitoring (CbM) applications. SPE provides a shared power and high bandwidth data architecture, where 10 Mbps data and power are shared on a low cost 2-wire cable at over 1000 meter distances.

Analog Devices has designed the industry's first 10BASE-T1L MAC-PHY (ADIN1110), which is a single pair Ethernet transceiver with embedded MAC. The ADIN1110 communicates with an embedded microcontroller using a simple SPI bus, which reduces both power consumption and firmware development time for the sensor.

In this article you will learn how to design a tiny but powerful sensor, as shown in Figure 1. This article will cover:

  • How to design a small, shared data and power communications interface
  • How to design an ultralow noise power supply for the sensor
  • Microcontroller and software architecture choices
  • Choosing a suitable MEMS vibration sensor
  • Integrating a digital hardware design and a mechanical enclosure
  • Examples for data collection UI at your PC

Figure 1. 10BASE-T1L single-pair Ethernet conditioning monitoring (vibration) sensor prototype.

How to Design a Small, Shared Data and Power Communications Interface

What Is PoDL?

Power and data are distributed on a single twisted pair using an inductor capacitor network, as shown in Figure 2. High frequency data is coupled to the data lines through series capacitors, which also protect the ADIN1100 10BASE-T1L PHY from DC bus voltages, as shown in Figure 2(a). Figure 2 shows a power supply connected at the PSE (power sourcing equipment) controller via a coupled inductor connected to a data line. The 24 VDC power biases the AC data bus as illustrated in Figure 2(b). In Figure 2(c), the current path is shown as IPWR between the PSE and PD (powered device), with the power extracted from the line using a coupled inductor at the CbM sensor node.

Figure 2. Basic principles of shared power and data wires (PoDL).

How Do I Design a Small PoDL Circuit?

Figure 2 includes the coupled inductor and series capacitors as the most basic components required for the PoDL operation. Additional components are required for robustness and fault tolerance.

Because the PoDL coupled inductors are nonideal components, some differential to common-mode conversion will occur. This common-mode noise will degrade signal quality. Connecting a common-mode choke close to the cable connector helps to mitigate against this nonideal behavior and protects the design against common-mode noise coming from the cable. The common-mode choke ampacity and DCR will need to be examined to ensure that they can support adequate power delivery for the sensor.

To protect against incorrect polarity cable installations—for example, a fault where the 24 VDC of the PSE PHY is wired to the 0 VDC of the PD PHY—a bridge rectifier diode is recommended. For EMC robustness, a TVS diode with working voltage greater than 24 VDC is required. Additional EMC components can be used (for example, high voltage capacitors on signal wires) if the sensor hardware design is larger.

Designing a small PoDL circuit using all these components can be challenging, but fortunately most vendors have competitively sized solutions for rectifier diodes, TVS diodes, and passive components. Generally, components with ultralow capacitance must be chosen to minimize signal distortion. The coupled inductor and capacitor values are recommended to have inductance and capacitance values of 220 µH and 220 nF respectively but can be larger in simulation or testing for design margin. A selection of very small size components that can be used for sensor design are listed in Table 1.

Table 1. Component Selection for PoDL Interface for Small Sensor Designs
Component Part Number Comment
Common-Mode Choke Würth 744242471 470 µH
TVS Bourns CDSOD323-T36SC
Coupled Inductor (power) Coilcraft LPD5030-224MRB 220 µH
Bridge Rectifier Diode Bourns CD-HD2006L
Series Capacitance Taiyo Yuden HMK107C7224KAHTE E 220 nF

The coupled inductor rated current needs to meet or exceed the total current requirements for the remotely powered MEMS sensor node. The LPD5030-224MRB is rated to at least 240 mA, greatly exceeding the requirement for a 10BASE-T1L sensor node. As the rated current requirement is relatively low, the inductor size can be reduced. Table 2 shows that the 4.8 mm × 4.8 mm LPD5030-224MRB is the smallest component that satisfies the 10BASE-T1L link requirements.

Table 2. Power Inductor—Constraints on Package Size
Parameter Implications for Inductor Package Size LPD5030-224MRB Value Comment
Sufficiently High Inductance Higher inductance values result in more internal windings and larger package size. 220 µH ±20% Recommended PoDL inductance.
Higher SRF (Self-Resonant Frequency) Higher SRF will mean less internal windings and smaller size.  5.53 MHz Larger than the maximum 10 Mbps data rate on the 10BASE-T1L link.
Higher Rated and Saturation Current Less internal windings, but larger package size.  0.24 A, 0.31 A Exceeds 10BASE-T1L MEMS sensor requirements.
Low DCR To achieve lower DCR, wires need to be thicker and have fewer windings. 3.3 Ω Hard to have thicker windings in a smaller package.

How Do I Check if My PoDL Circuit Will Work?

The IEEE 802.3cg-2019 standard for 10BASE-T1L outlines electrical specifications for a PHY to meet, including voltage levels, timing jitter, power spectral density, return loss, and signal droop (decay). The PoDL circuit affects the communication channels, with return loss and signal decay (or droop) being two significant factors.

Return loss is a measure of signal reflections likely to occur on a network and is caused by impedance mismatches at all locations along a cable link. Return loss is expressed in decibels and is of particular concern for high data rate or long cable reach (1700 m) communications used in 10BASE-T1L. Figure 3 (based on the work of Graber1 ) shows an LTspice® simulation circuit for the single-pair Ethernet (SPE) 10BASE-T1L standard (10SPE) physical layer or MDI. The simulation circuit includes termination resistance of 100 Ω ±10% for Analog Devices’ ADIN1110 or ADIN1100 10BASE-T1L Ethernet PHY/MAC-PHY. Signal coupling capacitance, power coupling inductors, common-mode choke and other EMC protection components are modelled. The power coupling inductance is nominally 1000 µH, which accounts for two 220 µH inductors with two windings each (880 µH plus margin). For some components, the recommended component value and tolerance range are added using LTspice Monte Carlo syntax. Figure 4 shows the corresponding Monte Carlo simulation waveform and limit line added using LTspice. The chosen components and tolerances will meet the return loss mask specification.

Figure 3. MDI return loss simulation using the LTspice Monte Carlo function.

Figure 4. Monte Carlo simulation waveform.

How to Design an Ultralow Noise Power Supply

Wired condition monitoring sensors have stringent noise immunity requirements. For CbM of railway, automation, and heavy industry (for example, pulp and paper processing), vibration sensor solutions need to output less than 1 mV of noise to avoid triggering a false vibration level at the data acquisition/controller. This means that the power supply design needs to output very little noise (low output ripple) into the measurement circuit (MEMS signal chain). The power supply design for the MEMS sensor must also be immune to noise coupled to the shared power and data cable (high PSRR).

Ensuring that a MEMS sensor can detect very small vibrations requires a very low noise supply. The ADXL1002 MEMS accelerometer has an output voltage noise density specification of 25 µg√Hz. During normal operation, the MEMS power supply needs to meet or exceed this specification to avoid degrading sensor performance.

Wired CbM sensors are typically powered from 24 VDC to 30 VDC, which requires a buck converter with high input range and high efficiency to minimize power dissipation and improve sensor long-term reliability. Due to nonideal capacitive loading, the buck converter voltage ripple can be 10s of millivolts, which is not suitable for supplying 3 V/5 V MEMS sensors. Using a common-mode choke or bulk capacitance can reduce the buck output ripple voltage. However, an ultralow noise LDO regulator is required at the buck output to ensure that a power supply with microvolts of noise is supplied to the MEMS sensor.

Powering a 10BASE-T1L Sensor Prototype

Figure 5 shows a power supply design for a digital wired MEMS sensor. The LT8618 is specifically designed for industrial sensors with:

  • Wide input range up to 60 V
  • Low output current 100 mA
  • Up to 90% efficiency
  • Tiny 2 mm × 2 mm LQFN packages

Figure 5 shows the LT8618 with a 24 VDC input, which is regulated to 3.7 V, and then input to the LT3042, which supplies 3.3 V to the MEMS sensor circuitry.

Figure 5. Power supply design for a digital wired MEMS sensor.

The LT3042 is a high performance ultralow noise LDO regulator with:

  • Ultralow rms noise at 0.8 µV rms (10 Hz to 100 kHz)
  • Ultrahigh PSRR (79 dB at 1 MHz)
  • Tiny 3 mm × 3 mm DFN packages

The article “How to Get the Best Results Using LTspice for EMC Simulation—Part 12 details an LTspice simulation circuit and discusses the EMC performance for the LT8618 and LT3042. Figures 19 and 20 from the article show simulation results for where an EMC disturbance is applied at the LT3042 input. This shows that the LT3042 has less than 200 µV of voltage ripple even in the presence of a 1 V p-p EMC disturbance at its input.

Integrating a Digital Hardware Design and a Mechanical Enclosure

A steel or aluminum enclosure is used to house a MEMS vibration sensor and provide solid attachment to monitored assets, as well as providing water and dust proofness (IP67). For vibration sensors, the natural frequencies of the enclosure must be greater than that of the applied vibration load measured by the MEMS sensor.

The frequency response plot for the ADXL1002 MEMS is shown in Figure 6. The ADXL1002 3 dB bandwidth is 11 kHz and it has a 21 kHz resonant frequency. A protective enclosure used to house the ADXL1002 needs to have a first natural frequency of 21 kHz or greater in the axis of sensitivity. Similarly, when designing a triaxial sensor, the natural frequencies of the mechanical enclosure need to be analyzed across vertical and radial directions.

Figure 6. MEMS and mechanical enclosure frequency response design goal.

Sensor prototypes are tested on modal shakers, which provide a controlled environment to set vibration test levels and sweep across frequency. The test results of sensor frequency response should closely follow the MEMS sensor information shown in Figure 6.

Modal Analysis

Modal analysis is a commonly used technique to provide a good understanding of the vibration characteristics of enclosures. Modal analysis provides the natural frequencies and normal modes (relative deformation) of a design. Finite element methods (FEM) using ANSYS or similar programs can be used to simulate the modal response of structures, helping to optimize the design and reduce the number of sensor prototype iterations.

Equation 1 is a simplification of the governing equation of modal analysis for a single degree of freedom system. The natural frequency is related to the mass matrix (M) and stiffness matrix (K) of the enclosure design. Equation 1 provides a simple intuitive way to evaluate a design. As you reduce the height of the sensor enclosure, the stiffness increases and the mass decreases, therefore the natural frequency increases. Also, as you increase the height of enclosure, the stiffness reduces and the mass increases, resulting in a lower natural frequency.

Equation 1

Most designs have multiple degrees of freedom. Some designs have hundreds. Using the finite element method provides quick calculations for Equation 1, which would be very time consuming to do by hand.

When simulating using ANSYS modal, both natural frequencies and mode participation factor (MPF) are output by the solver. The MPF is used to determine which natural frequencies are the most important for your design. A relatively high MPF means that a particular frequency may be a problem in your design. The examples shown in Table 3 illustrate that while a 500 Hz natural frequency is predicted in simulation for the x-axis, the mode is weakly excited, and is unlikely to be a problem. An 800 Hz strong mode is excited in the enclosure x-axis and will be a problem if the MEMS sensitive axis is orientated in the enclosure x-axis. However, this x-axis strong mode at 800 Hz is not of interest if the designer has a MEMS sensor PCB orientated to measure in the enclosure z-axis.

Table 3. Natural Frequency (Freq, Hz), Mode Participation Factor (MPF), and Axis of Interest
Mode Freq, Hz Axis MPF MPF Comment
1 500 x 0.001 Weak mode
2 800 x 0.45 Strong mode
3 1500 y 0.6 Strong mode
4 3000 y 0.002 Weak mode
5 10,000 z 0.33 Strong mode

Modal Analysis for a 10BASE-T1L Sensor Prototype

The article “How to Design a Good Vibration Sensor Enclosure Using Modal Analysis3 provides a detailed overview of modal analysis. While ANSYS is an efficient and sophisticated tool to analyze the modal response of structures, an understanding of the underlying equations will help in design. The underlying equations show that enclosure natural frequencies are influenced by both material choice and geometry. Cylindrical shapes with higher cross-sectional areas are better designed for higher rigidity and natural frequencies across all axes, compared to rectangular shapes. Rectangular shapes offer more options in sensor orientation and equipment attachment, compared to cylindrical shapes. Please refer to the article for examples and simulation results.

The 10BASE-T1L sensor prototype is designed using a triaxial 1 kHz bandwidth MEMS sensor (ADXL357), and the design goal is to create an enclosure that supports greater than 1 kHz. A rectangular enclosure design was created, as shown in Figure 7, and simulated using ANSYS. Table 4 shows the simulation results, with natural frequencies and mode participation factors indicating a minimum of 6 kHz bandwidth across all three axes. The design uses M6 lugs on the x-axis surface ends. Using these attachment points will ensure a solid equipment attach and best modal performance.

Figure 7. Enclosure for ADXL357 triaxial MEMS sensor and ADIN1110 10BASE-T1L MAC-PHY circuitry.

Table 4. Natural Frequency (Freq, Hz), Mode Participation Factor (MPF), and Axis of Interest for 10BASE-T1L Sensor Prototype
Mode Freq, Hz Axis MPF
1 11663 x 6080
2 6632 y 0.057
3 30,727 y 0.187
4 6080 z 0.370

Choosing a Suitable MEMS Vibration Sensor

What Specifications Do We Need to Look Out for When Choosing an Accelerometer?

While there is no official standard that categorizes vibration sensors, they can be assigned into categories using their effective resolution as shown in Figure 8. It can be clearly seen that MEMS accelerometers cover a small area compared to piezoelectric sensors. MEMS accelerometers have been designed for many application specific uses such as airbag crash detection, roll-over detection in vehicles, robotic arm positioning, platform stabilization, precision tilt detection, and many more. MEMS manufacturers developed sensors good enough to compete with IEPE vibration sensors only a few years ago and, as a result, this technology is still in its infancy, hence the smaller area of coverage for wired CbM installations, as shown on the left in Figure 8. However, this is expected to grow in the coming years as more MEMS vendors invest in condition monitoring vibration sensor solutions.

Figure 8. MEMS and piezo range of sensors for wired applications.

MEMS sensors offer some advantages that are proving disruptive in the world of vibration sensors. For example, the vast majority of MEMS sensors available on the market have three axes, integrated ADCs, digital filtering, excellent linearity, low cost, and low weight, and they are very small compared to piezoelectric or IEPE/ ICP sensors, as highlighted in Table 5. While IEPE sensors will continue to be used on the most critical assets, maintenance and facility managers are looking to extract deeper insights from less critical assets in a shift toward extracting better productivity, efficiency, and sustainability—that is, minimizing unplanned downtime and prolonging the lifetime of assets. Lower cost and performance sensors, whether they be MEMS or IEPE, will be used in such scenarios, which raises the question: is a single-axis IEPE sensor with superior noise and bandwidth performance always a better choice than a MEMS sensor with three axes

Table 5. Different Accelerometer Types and Most Important Design Specifications
Mode ADXL357 ADXL1002 Piezo Sensor (603C01)
Size (mm) 6 × 5.6 × 2.2 5 × 5 × 1.8 18 × 42.2
Integrated ADC Yes No No
No. Axes
3 1 1
Power Supply (V) 2.25 to 3.6 3.3 to 5.25 18 to 28
Interface SPI Analog Analog
Weight (g) <0.2 <0.2 51
Noise (µg/√Hz) 80 25 350
Bandwidth (kHz) 1 11 10
Current Consumption 200 µA 1 mA 2 mA to 20 mA

How Do 3-Axis MEMS Sensors Compare to IEPE Vibration Sensors?

Extensive testing has been carried out on the efficacy of triaxial MEMS accelerometers in identifying, with absolute confidence, specific faults that much higher performance single or even dual axis IEPE vibration sensors could not detect, as summarized in Table 6.1 Faults like a bent shaft, eccentric rotor, bearing issues, and cocked rotor could not be detected by a single-axis vibration sensor with absolute certainty unless some efforts were taken to understand specific anomalies prior to mounting. When only a single-axis vibration sensor is available, other CbM sensors, such as motor current or magnetic field, would be required to identify certain faults with more confidence.

A trade-off exists between a single-axis sensor with superior noise and bandwidth vs. triaxial sensing, and these extra axes can alleviate mounting position challenges as all the vertical, horizontal, and axial vibration will be detected, as well as offering deeper insights into the operation of an asset. According to the results presented in Table 6, most of the faults could not be confidently identified with a single axis sensor without reorienting and retesting, even if it has superior noise and bandwidth vs. a triaxial MEMS sensor.

Table 6. Summary of Fault Frequency Signatures for Commonly Occurring Machine Faults Detected by a Triaxial MEMS Accelerometer
Fault Spectrum Signatures Detectable with 1-Axis Notes
Detectable with z-axis (vertical) Detectable with y-axes (horizontal) Detectable with x-axes (axial) Confident detection with single axis
Imbalanced Load Fundamental (1×)
Fundamental (1×) 9×, 10× Yes Extra axes provide more confidence in measurement
Eccentric Rotor Fundamental (1×)
3×  No Axial signature clearly indicates rotor eccentricity
Cocked Rotor
Fundamental (1×)   3×, 4×, 5×, 6×, 7×, 8×, 9×, 10× No Axial signatures at high harmonics clearly identify cocked rotor
Bent Shaft Fundamental (1×) Fundamental (1×) Possible Axial signature clearly indicates bent shaft
BPFO 3× (BPFO), 4× 3× (BPFO), 4×   Yes Single axis measurement on the x-axis would miss this
BPFI Fundamental (1×) 5× (BPFI)   No Measuring on the x- or z-axis would miss this fault

What Other Vibration Sensors Are Available and How Do They Compare?

So, how do 3-axis MEMS sensors fit into the vibration sensor spectrum? Figure 9 shows an overview of the MEMS vibration sensors available today based on their noise vs. bandwidth. IEPE sensors are included for reference and to help highlight where exactly MEMS sensors fit onto the vibration sensor spectrum. It is clear that the different type of MEMS sensors naturally form clusters that we can use to assign potential use cases—for example, the lowest cost sensors (MEMS triaxial) would be used on lower criticality assets whereas the highest cost sensors (IEPE) will be used on the highest criticality assets. Single-axis IEPE sensors have been in use for decades and cover all areas from low to high criticality applications and are widespread in terms of cost and performance, as shown in Figure 9. It is clear to see that triaxial IEPE sensors have similar performance to triaxial MEMS sensors but at a much higher cost. For the low criticality assets cluster, it is not viable to use triaxial IEPE sensors due to their high cost, but this further highlights the point that triaxial MEMS sensors can compete with some triaxial IEPE sensors in terms of noise and bandwidth.

Figure 9. Comparison of vibration sensors clustered into triaxial MEMS and IEPE and single-axis MEMS and IEPE.

What Sensor Was Most Suitable for the Deployable Single-Pair Ethernet Conditioning Monitoring Sensor and Why?

The deployable single-pair Ethernet condition monitoring sensor was designed to house a vibration sensor, but the system architecture allows for many types of sensors such as temperature, pressure, sound, position, etc. to be used whether they be analog or digital output with minimal changes to the microcontroller firmware. The vibration sensor had to be small, digital out (SPI or I2C) with high levels of integration (amplifiers, ADCs) in order to meet the size and performance requirements of the deployable single-pair Ethernet condition monitoring sensor. A 3-axis, digital output MEMS accelerometer was chosen based on the specifications shown in Table 5.

A low noise triaxial sensor was chosen instead of a lower noise, wider bandwidth single axis MEMS sensor in order to provide more diagnostic insights (3-axis vs. 1-axis) and ease mounting challenges associated with single-axis sensors. The next key consideration is power consumption, and it is clear to see ADXL357 will produce less self-heating effects inside an IP6x module compared to the other sensors as the ADXL357 does not need an ADC or op amp, reducing the overall solution size and BOM cost. A reduced solution size ensures a small mechanical enclosure and good modal frequency performance, as explained in the section on modal analysis.

Higher performance, single axis, wide bandwidth (11 kHz to 23 kHz) MEMS sensors, such as the ADXL100x family with up to 14-bit resolution could be seamlessly integrated, but this would likely require an external ADC to preserve performance as most low power microcontrollers only have integrated 12-bit ADCs. However, with a suitable microcontroller, oversampling and decimation could be used to increase resolution above 12 bits, meaning a single-axis, analog output MEMS accelerometer could be integrated to the existing system with minimal effort. Please note, if you require better than 13-bit resolution, you must use an analog output MEMS or IEPE sensor, as shown in Table 7.

Table 7. Comparison of High Performance MEMS and IEPE Sensors
Sensor Range (±g) Output Range Peak-to-Peak (V) Channel Count Linearity (±%FSR) NSD (µg/√Hz) 1/f Corner (Hz) Flat BW (kHz) Noise Flat BW (µg rms) DR @ Flat BW (dB) ENOB at Flat BW Resonant Freq (kHz)
ADXL1002 50
4 1 0.1 25 0.1 11 2622.02 82.60 13.43 21
ADXL1004 500 4 1 0.25 125 0.1 24 19364.92 85.23 13.87 45
40 1.6 3 ;0.1 110 0.1 1 3478.51 78.20 12.70 5.5
PCB 621B40 500 10 1 1 10 1000 30 1732.05 104.95 17.14 85
PCB 352C04 500 10 1 1 4 400 10 400.00 118.93 19.46 50
PCB 333B52 5 10 1 1 0.4 1000 3 21.91 98.50 16.07 20

Microcontroller and Software Architecture Choices

A simple vibration sensor-based MQTT architecture is shown in Figure 10 and a high level block diagram for the deployable single-pair Ethernet conditioning monitoring sensor interfaced to a PC or Raspberry Pi is shown in Figure 11. Message Queue Telemetry Transport (MQTT) is a lightweight messaging protocol for the Internet of Things that allows network clients to distribute telemetry data in low bandwidth environments. MQTT is considered lightweight due to its messages having a small code footprint. The publish and subscribe messaging transport is ideal for connecting remote devices with a minimal code footprint and network bandwidth. MQTT is used in a vast array of industries from oil and gas, automotive, telecommunications, and manufacturing. Publishers send messages and subscribers receive messages they are interested in. Brokers pass messages from publishers to subscribers. Some MQTT brokers handle millions of concurrently connected MQTT clients, and this is one of the attractive features whereby many sensors could be connected to one SPE device creating a pipeline of sensor data as shown in Figure 10. Both publishers and subscribers are MQTT clients that can only communicate with the MQTT broker. MQTT clients can be any device such as Arduino, Raspberry Pi, ESP32, or an application like Node-RED or MQTTfx.

Figure 10. MQTT Simple publish/subscribe architecture.

Figure 11. Deployable single-pair Ethernet condition monitoring sensor block diagram.

The first four blocks in Figure 11 consist of a sensor, microcontroller, MAC-PHY, and media converter. The sensor is a digital output triaxial MEMS sensor that can detect vibrations. Any standard low power microcontroller, such as MAX78000 or MAX32670, with an SPI interface can be used to read data from the ADXL357. The MAX78000 has the added advantage of providing capability for ultra low power edge AI processing, with a built-in convolutional neural network (CNN) hardware accelerator.

The measured vibration data is placed into an MQTT topic for transmission to the MAC-PHY, again, over SPI. The low cost Cortex® -M4 microcontroller can be used to read/write to the ADIN1110 MAC-PHY over SPI to enable different modes and configurations such as PoDL ON or OFF, T1L special, main or subnode, 1 V or 2.4 V. The ADIN1110 converts MQTT data topics to 10BASE-T1L format and transmits over 300 m of IP67 rated cable with advanced shielding for robustness over 1700 m. The media converter then converts data from 10BASE-T1L to 10BASE-T format so a PC or Raspberry Pi can interpret the data, then process and display it.


ADIN1110 is a robust, single port, low power 10BASE-T1L Ethernet MAC-PHY transceiver targeting industrial applications. Featuring an integrated MAC interface, the ADIN1110 enables direct connectivity with a variety of host controllers via SPI. This SPI communications channel enables the use of lower power processors without an integrated MAC, which provides for the lowest overall system level power consumption. The ADIN1110 is designed for edge node sensors and field instruments deployed in building, factory, and process automation. The device operates from a single power supply rail of 1.8 V or 3.3 V. Supporting both 1.0 V and 2.4 V amplitude modes of operations and external termination resistors, the ADIN1110 supports use within intrinsically safe environments. The programmable transmit levels, external termination resistors, and independent receive and transmit pins make the ADIN1110 suited to intrinsic safety applications.

The vision of Ethernet to the field or edge is to connect all sensors and actuators to a converged IT/OT network. To achieve this vision, there are system engineering challenges, as some of these sensors are limited in power and space. There is a growing market of low power and ultra low power microcontrollers with significant internal memory capabilities for sensor and actuator applications. But most of these processors have one thing in common—with no integrated Ethernet MAC, they don’t support an MII, RMII, or RGMII media independent (Ethernet) interface. A traditional PHY cannot be connected to these processors/microcontrollers.

The firmware implementation for the deployable single-pair Ethernet condition monitoring sensor is shown in Table 8.

Table 8. Firmware Code Size
Memory Type Description Size (kB)
Total RO (Constants) Code + RO data 110
Total RAM Size RW data + zero initialized data 121
Total ROM Size Code + RO data + RW data 110

Most low cost Cortex-M4 microcontrollers with sufficient memory would be suitable for this application. The SPE condition monitoring sensor software architecture can be seen in Figure 13, and it consists of multiple elements with a corresponding block diagram shown in Figure 12. The microcontroller can easily handle the operating system (FreeRTOS) and MQTT libraries, as well as the lightweight IP stack or lwIP, a small independent open-source implementation of the TCP/IP protocol suite designed to reduce RAM usage while still having full-scale TCP. FreeRTOS provides an open-source, very well documented and supported operating system that makes it easy to add new functional blocks of code. The aim of the lwIP TCP/IP implementation is to provide a full-scale transmission control protocol (TCP) while reducing resource usage, making lwIP ideal for use in embedded systems with 10s of kBs of free RAM and roughly 40 kB of code ROM. There are also add-on applications such as MQTT client that was used to provide MQTT functionality. The MQTT block is configured in publish/subscribe mode providing a streamlined, high efficiency solution. The ADIN1110 driver needs to communicate with an address resolution protocol (ARP) block before communicating with the lwIP stack in order to ensure seamless network communications between the microcontroller and ADIN1110.

Figure 12. Software architecture.

Figure 13. Sensor block diagram and code development requirements for each block.

PyMQTT is a Python-based library extension for integrating an MQTT client into a web application. It is used to subscribe to the SPE sensor, extract data and display it in a GUI, so, it effectively acts as a wrapper around the paho-mqtt package to simplify MQTT integration into a Python application.

ADIN2111: Low Complexity, 2-Port Ethernet Switch with Integrated 10BASE-T1L PHYs

ADIN2111 adds Ethernet connectivity to every node in a factory/building using long reach 10BASE-T1L technology, simplifying network management. ADIN2111 supports low power edge node designs and interfaces to a wide variety of host controllers via SPI. A two port switch such as ADIN2111 could be used for daisy-chaining data between constrained edge nodes in a line or ring topology. Line and ring topologies are the dominant architecture in industrial deployments. Each device will require two ports for input and output so each device will need a switch and two 10BASE-T1L PHYs as provided by ADIN2111.

Figure 14. ADIN2111 functional block diagram.

ADIN2111 comes with a suite of diagnostic features and can monitor link quality and detect faults to reduce commissioning time and system downtime. It enables realtime fault detection and fault location detection with 2% accuracy over 1 km of cable helping to reduce downtime and commissioning time. ADIN2111 enables networks of sensors, actuators, and controllers to be connected in a line or ring topology while utilizing existing deployed single twisted pair cabling infrastructure.

Data Acquisition and GUI

Vibration data can be visualized in the time and frequency domain using the Python-based GUI as shown in Figure 15. The Python GUI is an executable, so no code development is required unless you want to modify it.

Figure 15. SPE sensor measuring an imperceptible 20 Hz vibration.

In order to verify the performance of the SPE sensor system, a series of tests were carried out. Imbalance load tests are a reliable method for testing the performance of a vibration sensor as the time and frequency domain signatures are easily recognized. Figure 16 shows the time domain data on the left and frequency domain data on the right. On the y and z axes there is a clear sinusoidal signal correlating to the vibrations measured from the imbalanced load at the motors rpm or fundamental speed. This is because the y and z axes were positioned to measure the largest vibration responses from the imbalanced motor. The x axis does measure some repetitive data but it is not sinusoidal and an order of magnitude or more lower in amplitude than the y and z axes. However, on the frequency domain plots the x-axis clearly shows an imbalance signature, as do the y and z but on a much higher magnitude.

Figure 16. SPE sensor detecting an imbalanced load from a 9 VDC motor with an eccentric weight on the rotor.

In order to investigate the noise performance of the system another test was used whereby a tone generator was placed on the same baseplate as the SPE sensor. Vibrations are imperceptible to the human hand, but the ADXL357 coupled with the 10BASE-T1L communications pipeline can reliably detect anomalies on all three axes.

Figure 17. 750 Hz vibration tone measurement.


Analog Devices’ deep domain expertise in condition monitoring, coupled with a strong portfolio of sensors, power devices, and Ethernet connectivity, enables the designer to get to market with the right design the first time. The ADIN1110 single pair Ethernet MAC-PHY is the designer’s best option in creating a vibration sensor, which can be used to access asset health information from anywhere and at anytime with Ethernet IP addressability.

This article presents the tip of the iceberg in terms of Analog Devices sensor, power, and microcontroller offerings. Please visit ADI’s condition-based monitoring page for more information.


1Steffen Graber. “10 Mb/s Single Twisted Pair Ethernet, 10BASE-T1L MDI Return Loss.” IEEE, May 2018.

2Richard Anslow and Sylvain Le Bras. “How to Get the Best Results Using LTspice for EMC Simulation—Part 1.” Analog Devices, Inc., November 2021. 

3Richard Anslow. “How to Design a Good Vibration Sensor Enclosure Using Modal Analysis.” Analog Devices, Inc., February 2022.


Richard Anslow

Richard Anslow

Richard Anslow is a senior manager, working in the field of software systems design engineering within the Industrial Automation Business Unit at Analog Devices. His areas of expertise are condition-based monitoring, motor control, and industrial communication design. He received his B.Eng. and M.Eng. degrees from the University of Limerick, Limerick, Ireland. Recently, he completed a postgraduate program in AI and ML with Purdue University.

Chris Murphy

Chris Murphy

Chris Murphy is a field technical leader with the European Centralized Applications Center, based in Dublin, Ireland. He has worked for Analog Devices since 2012, providing design support on motor control and industrial automation products. He holds a Master of Engineering in electronics by research and a Bachelor of Engineering in computer engineering.