A method for reduction of Acoustic Emission (AE) data with application in machine failure detection and diagnosis. (December 2017)

• Record Type: Journal Article
• Authors: Vicuña, Cristián Molina; Höweler, Christoph
• Is Part Of: Mechanical systems and signal processing. Volume 97(2017)
• Page Start: 44
• Page End: 58
• Publication Date: 2017-12
• Subjects: Acoustic emissions -- Data reduction -- Failure detection -- Failure diagnosis; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2017.04.040
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: TriSignal: a method for large reduction of Acoustic Emissions (AE) data. TriSignal spectrum provides same information as envelope spectrum. Method allows failure detection and diagnosis. Application examples for gear and slow-speed bearing failure diagnosis. Abstract: The use of AE in machine failure diagnosis has increased over the last years. Most AE-based failure diagnosis strategies use digital signal processing and thus require the sampling of AE signals. High sampling rates are required for this purpose (e.g. 2 MHz or higher), leading to streams of large amounts of data. This situation is aggravated if fine resolution and/or multiple sensors are required. These facts combine to produce bulky data, typically in the range of GBytes, for which sufficient storage space and efficient signal processing algorithms are required. This situation probably explains why, in practice, AE-based methods consist mostly in the calculation of scalar quantities such as RMS and Kurtosis, and the analysis of their evolution in time. While the scalar-based approach offers the advantage of maximum data reduction; it has the disadvantage that most part of the information contained in the raw AE signal is lost unrecoverably. This work presents a method offering large data reduction, while keeping the most important information conveyed by the raw AE signal, useful for failure detection and diagnosis. The proposed method consist in the construction of a synthetic, unevenly sampled signal which envelopes the AE bursts present on the raw AE signal in a triangular shape. The constructed signal – which we call TriSignal – also permits the estimation of most scalar quantities typically used for failure detection. But more importantly, it contains the information of the time of occurrence of the bursts, which is key for failure diagnosis. Lomb-Scargle normalized periodogram is used to construct the TriSignal spectrum, which reveals the frequency content of the TriSignal and provides the same information as the classic AE envelope. The paper includes application examples in planetary gearbox and low-speed rolling element bearing.