Improving the Efficiency and Sustainability of Electric Motors

­Industry 4.0 is seen as the new era of manufacturing, combining technology, robotics, artificial intelligence, and automation to create an efficient and effective manufacturing process. Industrial use cases represent 37% of energy used worldwide, with 70% consumed by motors.

Over recent years, there has been an intensive effort in the design of induction motors with enhanced efficiencies due to the amount of energy consumed by motors. However, another factor strongly affects motor efficiency, which is often disregarded. Typically, industrial electric motors operate between 50% and 85% efficiency. The motor health condition can cause a significant loss of energy efficiency. The rated efficiency values provided by the manufacturer are only valid assuming the motor condition is optimum, that is, there are no significant anomalies, defects, or faults during the operation. If a fault is present in the machine, even if it is in its early stages of fault development, motor efficiency will be reduced.

It is well known that electric motor efficiency is defined as the ratio of its useful power output to its total power input:

The two major motor power losses are:

^► Intrinsic power loss

Includes copper losses (resistive, skin effect), iron losses (eddy current, hysteresis), and mechanical losses (friction, windage). Intrinsic power losses can be reduced in the motor design phase.

^► Anomaly power loss

Includes extra power losses caused by unhealthy motor conditions, such as anyone or multiple electrical, electromechanical or mechanical motor faults. Anomaly power losses can be minimized by keeping the motor operation in optimum condition and this is heavily related to motor maintenance schemes.

Motors in an unhealthy condition can run at low efficiency for a long time before the motor faults become a motor failure and cause the machine to breakdown.

This can cause a significant loss of energy. The effect of different types of bearing faults on the efficiency of induction motors has been investigated. Four types of bearing faults have been tested: Fault 1 — a crack in the outer race, Fault 2 — a hole in the outer race, Fault 3 — deformation of the protective shield, and Fault 4 — a corrosion of the bearing. Example photo of bearing fault type of Fault 1 is shown in Figure 1.

Equation 1: Motor efficiency formula

An experimental setup consisted of a 2.2 kW three-phase induction motor fed by the main power supply control unit and coupled with a break. Motor input current, voltage, and phase were measured to calculate the motor input power. Motor load torque and rotation speed were measured to calculate the motor output power. The motor efficiency is calculated as the ratio between the motor output mechanical power and motor input electrical power. Figure 1 shows how the motor efficiency changes over the different load condition. As illustrated, bearing faults can cause a 1.5% efficiency reduction in full load condition and a 4% efficiency reduction in light load condition.

It has been shown that motor faults, such as rotor bar faults, stator winding faults, motor shaft misalignment faults, and soft foot and cooling fan motor faults can cause motor efficiency reduction. Figure 2 shows how different motor faults affect motor efficiency.

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Figure 2: Different types of motor faults’ impact on motor efficiency

ADI OtoSense SMS Explained

OtoSense SMS is an AI-based, full turnkey, hardware and software solution for CbM and predictive maintenance of industrial electrical motors. It monitors the condition of electric motors by combining best-in-class sensing technologies with state-of-the-art data analysis.

The solution consists of a hardware subsystem and a software subsystem, which includes a cloud platform, web application, and mobile application. A machine learning-based motor fault diagnosis AI algorithm is part of the cloud platform. Figure 3 shows the OtoSense SMS system diagram.

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Figure 3: OtoSense SMS system diagram

OtoSenss SMS integrates several high-performance sensors developed by ADI, including:

^►  Two low-noise, high-frequency MEMS accelerometers ADXL1002 for both x-and z- axis vibration sensing.

^►  Two high-accuracy, 16-bit digital temperature sensor ADT7420 for motor frame and ambient temperature sensing.

In addition to:

^►  One magnetic field sensor for motor speed sensing and motor electrical fault diagnosis.

^►  One Wi-Fi processor for data collection and data packing for 2.4 GHz Wi-Fi data transfer.

OtoSense SMS sensor senses and interprets machine data. It can detect many common faults in all areas of the motor, including the power system, stator winding, rotor, air gap, shaft and bearings. 

Operational Efficiency

Proper maintenance helps to meet maximum economic profitability, as the occurrence of motor failures is reduced and unscheduled downtime can be avoided. Additionally, the efficiency of the motors plays a fundamental role in cost savings per operation since a high-efficiency motor consumes less electrical energy than a motor with standard efficiency. Studies have shown the extent to which the efficiency of the machine is compromised by the presence of different types of failure, specifically, rotor failures, stator winding asymmetries, insulation system failures, imbalance/misalignments, and ventilation system failures.

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