The rotating machinery is a common class of machinery in the industry. The root cause of faults in the rotating machinery is often faulty rolling element bearings. This paper presents a novel technique using artificial neural network learning for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (harmmean and median), whic...

Because both the inner and outer rings rotate, the intershaft bearings used in gas turbines do not have fixed bearing housings. As a result, the vibration of intershaft bearings cannot be measured directly. Therefore, a vibration signal can only be collected through indirect measurement. First, it must be transferred to adjacent bearings through the shafting. Then, it should be transferred by t...

Huntington's disease (HD) is a congenital, progressive, neurodegenerative disorder characterized by cognitive, motor, and psychological disorders. Clinical diagnosis of HD relies on the manifestation of movement abnormalities. In this study, we introduce a mathematical method for HD detection using step spacing. We used 16 walking signals as control and 20 walking signals as HD. We took a s...

Statistical Energy Analysis (SEA) is a suitable tool to predict vibration stationary responses. It is roughly categorized into analytical, FEM based, and experimental SEA due to the process of model construction. SEA is expected to be used for designing machines with optimal vibration transfer paths. However experimental SEA model accuracy especially in low frequency is highly dependent on expe...

This paper presents a method for extracting features in the wavelet domain from the vibration velocity signals of washing machines, focusing on the transient (non-stationary) part of the signal. These features are then used for classification of the state (acceptable-faulty) of the machine. The performance of this feature set is compared to features obtained through standard Fourier analysis of...

Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel fe...

A Optical Fiber Vibration pre-Warning System (OFVWS) has an ability to detect and identify vibration signals by using a optical fiber cable lain along with pipes, hence it has been widely applied in some safety fields, such as the pipeline transportation, national defense, military base and so on. In the application of the OFVWS, one of the key issues is to identify harmful mechanical vibration...

When compared with a traditional planetary gearbox, the split torque gearbox (STG) potentially offers lower weight, increased reliability, and improved efficiency. These benefits have driven the helicopter manufacturing community to develop products using the STG. However, this may pose a challenge for the current gear analysis methods used in Health and Usage Monitoring Systems (HUMS). Gear an...

This paper introduces a new bearing fault detection and diagnosis scheme based on hidden Markov modeling (HMM) of vibration signals. First features are extracted from amplitude demodulated vibration signals obtained from both normal and faulty bearings. The features are based on the reflection coefficients of the polynomial transfer function of the autoregressive model of the vibration signal. ...