Analysis and application of weak guided wave signal detection based on double Duffing oscillators is the most recent accomplishment of Professor Ma Hongwei and his team, which was just published in the prestigious journal Mechanical Systems and Signal Processing. Using the sensitivity of chaotic systems, the authors expertly built a double chaotic detection system to achieve quantitative detection of weak ultrasonic-guided wave signals. Theoretically, this approach can be used to identify and measure weak guided wave signals.

Based on the principle of low-frequency stress wave propagation in bounded structures, ultrasonic-guided wave testing is a nondestructive testing technology. Flaw detection of slender structures like pipes, railways, and steel bars is a common use of this technology due to its many benefits, including its long detection distance, fast speed, low cost, and so on.

A major difficulty with ultrasonic-guided wave detection arises when the defect is far away or small; the defect echo will act as a weak signal beneath heavy noise, making identification difficult at best. Conventional detection techniques first need to filter out background noise before they can properly identify faulty signals, and this noise-reduction processing can sometimes have a negative impact on the intended signal. To accomplish the detection of the weak guided wave signal, this method makes direct use of the sensitivity of the chaotic system as well as its immunity to noises in signals.

In recent years, many weak signal detection needs have been found in various industries, including mechanical system early fault diagnosis, geological exploration, radar detection, biological signal detection, ocean detection, and so on, providing a broad field for the application of this technology.

Cheng Mengfei, a PhD student at Jinan University and Dongguan University of Technology, is the first author of the paper; Professors Ma Hongwei and Zhang Weiwei serve as co-corresponding authors; and Professor Wu Jing of DGUT's Mechanical Engineering Department is also a co-author. The research work was funded by the National Natural Science Foundation of China, the Key Laboratory of Robotics and Intelligent Equipment in Guangdong Universities, and the Innovation Center for Robotics and Intelligent Equipment of Dongguan University of Technology.