Bearing fault diagnosis under unknown variable speed via gear noise cancellation and rotational order sideband identification. (October 2015)

• Record Type: Journal Article
• Authors: Wang, Tianyang; Liang, Ming; Li, Jianyong; Cheng, Weidong; Li, Chuan
• Is Part Of: Mechanical systems and signal processing. Volume 62/63(2015)
• Page Start: 30
• Page End: 53
• Publication Date: 2015-10
• Subjects: Bearing fault diagnosis -- Gear signal interferences -- Unknown time-varying rotational speed -- Instantaneous dominant meshing frequency multiple -- Rotational order sideband; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2015.03.005
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: The interfering vibration signals of a gearbox often represent a challenging issue in rolling bearing fault detection and diagnosis, particularly under unknown variable rotational speed conditions. Though some methods have been proposed to remove the gearbox interfering signals based on their discrete frequency nature, such methods may not work well under unknown variable speed conditions. As such, we propose a new approach to address this issue. The new approach consists of three main steps: (a) adaptive gear interference removal, (b) fault characteristic order (FCO) based fault detection, and (c) rotational-order-sideband (ROS) based fault type identification. For gear interference removal, an enhanced adaptive noise cancellation (ANC) algorithm has been developed in this study. The new ANC algorithm does not require an additional accelerometer to provide reference input. Instead, the reference signal is adaptively constructed from signal maxima and instantaneous dominant meshing multiple (IDMM) trend. Key ANC parameters such as filter length and step size have also been tailored to suit the variable speed conditions, The main advantage of using ROS for fault type diagnosis is that it is insusceptible to confusion caused by the co-existence of bearing and gear rotational frequency peaks in the identification of the bearing fault characteristic frequency in the FCO sub-order region. The effectiveness of the proposed method has been demonstrated using both simulation and experimental data. Our experimental study also indicates that the proposed method is applicable regardless whether the bearing and gear rotational speeds are proportional to each other or not. Highlights: We propose a method for fault diagnosis under unknown variable speed conditions. The ANC method is enhanced for gear interference removal based on IDMM trend. The ANC result is converted into FPA domain leading to FCO representation via IFCF. A ROS-based strategy is proposed to identify bearing (shaft) rotational order. The ROS strategy helps discern the bearing rotational frequency.