Predictive diagnostic maintenance solution for improving the efficiency and sustainability of electric motors

Enhancing operational efficiency with condition monitoring and predictive maintenance 

Industry 4.0 is regarded as a new era in manufacturing, integrating technology, robotics, artificial intelligence, and automation to create highly efficient and productive manufacturing processes. Industrial applications account for 30% of global energy consumption, with 70% of this energy consumed by motors. If motors operate at maximum efficiency, global electricity consumption could potentially be reduced by 10%. But how can this goal be achieved? Evidence shows that improving operational efficiency through Condition-Based Monitoring and Predictive Maintenance (CbM/PdM) optimizes performance in productivity, quality, and logistics management, helping to meet sustainability metrics.

In recent years, given the high energy consumption of motors, manufacturers have invested significant effort in designing more efficient induction motors. However, one factor that significantly impacts motor efficiency is often overlooked. Typically, industrial motors operate at efficiencies ranging between 50% to 85%, and poor motor health can lead to a notable decline in energy efficiency. The rated efficiency values provided by manufacturers are only valid under ideal motor conditions, meaning no significant anomalies, defects, or faults during operation. If a fault occurs, even in its early stages, motor efficiency will be compromised.

Motor power losses are primarily divided into two categories: intrinsic power losses, such as copper losses (resistive, skin effect), iron losses (eddy current, hysteresis), and mechanical losses (friction, windage). Intrinsic power losses can be minimized during the motor design phase. Additionally, there are anomaly power losses, which include extra losses caused by suboptimal motor conditions, such as rotor bar faults, stator winding faults, motor shaft misalignment faults, soft foot faults, and cooling fan motor faults, all of which reduce motor efficiency. Keeping motors in optimal operating conditions can minimize anomaly power losses, which is closely tied to maintenance strategies.

ADI OtoSense SMS: An AI-based complete hardware and software solution

ADI's OtoSense SMS is a comprehensive AI-based hardware and software full turnkey solution for CbM and PdM of industrial electrical motors. It combines advanced sensing technology with data analytics to monitor motor conditions. The solution consists of a hardware subsystem and a software subsystem, the latter of which includes a cloud platform, web application, and mobile application, with the cloud platform featuring a machine learning-based AI algorithm for motor fault diagnosis.

OtoSense SMS integrates multiple high-performance sensors developed by ADI, including two low-noise, high-frequency MEMS accelerometers (ADXL1002) for x-axis and z-axis vibration sensing, and two high-accuracy, 16-bit digital temperature sensors (ADT7420) for monitoring motor frame and ambient temperature sensing. Additionally, it includes a magnetic field sensor for motor speed sensing and electrical fault diagnosis, as well as a Wi-Fi processor for data collection and data packing for 2.4 GHz Wi-Fi transfer. OtoSense SMS is an outstanding solution in the market for sense and interpret machine data.

Using OtoSense SMS to improve motor operational efficiency helps maximize economic benefits by reducing motor faults and avoiding unplanned downtime. Furthermore, motor efficiency plays a fundamental role in cost savings per operation, as high-efficiency motors consume less electrical energy than standard-efficiency motors. Research shows that different types of failures impact machine efficiency to varying degrees, including rotor failures, stator winding asymmetry, insulation system failures, imbalance/misalignments, and ventilation system failures.

The cloud platform provides deep insights into motor operating conditions and maintenance needs. Leveraging proprietary OtoSense SMS predictive maintenance analytics, users can identify the nine most common motor faults at an early stage and address them before they affect motor operation. For each motor fault, a Fault Score Index (FSI) is calculated to indicate the severity of motor faults. The FSI ranges from 0 to 10. An FSI above 7 indicates the motor is in good health condition, while an FSI between 5 and 7 signals an early-stage fault, triggering a low-severity warning notification via email. Motors in a warning state can still operate normally for a certain period, but since they are no longer in optimal condition, their operational efficiency will decline.

The significant economic benefit of OtoSense SMS lies in cost reduction through improved motor efficiency. As more enterprises focus on operational efficiency, reducing unplanned downtime, and achieving sustainability, adopting CbM and PdM technologies has become imperative. OtoSense SMS technology provides the real-time monitoring of motor status, early failures detection, and recommended early troubleshooting actions. Early detection and elimination of motor faults not only prevent unexpected failures and shutdowns but also ensure motors operate at high efficiency, leading to energy savings. Enterprises aiming to enhance operational efficiency and achieve sustainability goals in the next decade must implement these recommendations.

For example, OtoSense SMS can be used for compressor monitoring. Compressors are among the most critical pieces of equipment in factories, and OtoSense SMS devices can be installed to enable 24/7 continuous monitoring. OtoSense SMS can also be applied to material handling systems, such as airport passenger baggage conveyors, which are high-density motor-driven applications. By adopting the OtoSense SMS solution, early-stage bearing faults can be detected, and warning notifications can be sent to customers, preventing permanent bearing damage and avoiding unexpected system downtime, thereby saving operational energy consumption and maintenance costs.

High-accuracy digital temperature sensor with breakthrough performance

The ADT7420, part of the ADI OtoSense SMS solution, is a high-accuracy digital temperature sensor in a 4 mm × 4 mm LFCSP package, delivering breakthrough performance across a wide industrial temperature range. It features an internal bandgap temperature reference, a temperature sensor, and a 16-bit ADC for temperature monitoring and digitize the temperature with a resolution of 0.0078°C. The default ADC resolution is set to 13 bits (0.0625°C). The ADC resolution is user-programmable and can be adjusted via a serial interface.

The ADT7420 is guaranteed to operate within a supply voltage range of 2.7 V to 5.5 V. At 3.3 V, the average supply current is typically 210 µA. The ADT7420 includes a shutdown mode that powers down the device, with a typical shutdown current of 2.0 µA at 3.3 V. Its rated operating temperature range is -40°C to +150°C.

Pins A0 and A1 on the ADT7420 are used for address selection, providing four possible I2C addresses. The CT pin is an open-drain output that becomes active when the temperature exceeds a programmable critical limit. The INT pin is also an open-drain output that activates when the temperature exceeds a programmable limit. The INT and CT pins can operate in comparator and interrupt event modes.

To accelerate product development, ADI also offers compatible evaluation boards, including the EV-TempSense-ARDZ, a platform for evaluating temperature sensors with ±0.1°C, ±0.25°C, and ±0.5°C accuracy. Additionally, the EVAL-ADT7420-PMDZ is a PMOD board supporting ±0.25°C temperature measurement, while the EV-COG-AD3029 is an ADuCM3029 Cog development platform for ultra-low-power applications. The EV-COG-AD4050 is the ADuCM4050 variant of the Cog development platform, designed for ADI's ultra-low-power technology across MCU and RF transceiver portfolios.

MEMS accelerometer with ultra-low noise density

ADI's ADXL1001/ADXL1002 are low-noise, high-frequency ±100 g/±50g MEMS accelerometers that deliver ultra-low noise density across an extended frequency range with two full-scale options, optimized for industrial condition monitoring. The ADXL1001 (±100 g) and ADXL1002 (±50 g) feature typical noise densities of 30 µg/√Hz and 25 µg/√Hz, respectively. Both accelerometers offer stable and repeatable sensitivity and can withstand external shocks of up to 10,000 g.

The ADXL1001/ADXL1002 include integrated full electrostatic self-test (ST) and an overrange (OR) indicator, enabling advanced system-level features for embedded applications. With low power consumption and single-supply operation from 3.3 V to 5.25 V, they also support wireless sensing product design. The ADXL1001/ADXL1002 come in a 5 mm × 5 mm × 1.80 mm LFCSP package and operate over a temperature range of -40°C to +125°C.

The ADXL1001/ADXL1002 are single in-plane axis accelerometers with analog output, offering a linear frequency response range from DC to 11 kHz (3 dB point) and a resonant frequency of 21 kHz. They feature ultra-low noise density, overrange sensing with DC coupling for fast recovery, comprehensive electromechanical self-test, and sensitivity performance. Temperature sensitivity stability is 5%, linearity is ±0.1% of full-scale range, and cross-axis sensitivity is ±1% (ZX) and ±1% (YX). They operate on a single supply with a ratiometric output voltage and consume only 1.0 mA of power. A power-saving standby mode with fast recovery is also supported, and the devices are RoHS-compliant. The ADXL1001/ADXL1002 are widely used in condition monitoring, predictive maintenance, asset health, test and measurement, and Health and Usage Monitoring Systems (HUMS). ADI also offers the ADXL1001/ADXL1002 evaluation board for customer use.

Conclusion

By adopting predictive diagnostic and maintenance technologies, the efficiency and sustainability of motor systems can be significantly improved. Combining sensor data, edge computing, and artificial intelligence not only enables real-time monitoring of motor conditions and proactive fault prevention but also extends equipment lifespan, reduces energy consumption, and lowers maintenance costs. The ADI OtoSense SMS introduced in this article exemplifies how predictive diagnostic maintenance for motors can be achieved. Such innovative solutions are becoming key enablers for smart manufacturing and green industrial transformation, providing enterprises with the foundation for stable operations and sustainable development.