R.K. Diagnostics – Publications Overview

TECHNICAL PUBLICATIONS

Emphasizing bearings’ tones for prognostics

Bearing failure is one of the foremost causes of breakdowns in rotating machinery and such failure can be catastrophic, resulting in costly downtime. One of the key issues in bearing prognostics is to detect the defect at its incipient stage and alert the operator before it develops into a catastrophic failure.

Vibration analysis can be applied very effectively to rolling element bearings condition monitoring in all three aspects (detection, diagnosis, and prognosis).

Signal de-noising and extraction of the weak signature are crucial to bearing prognostics since the signature of a defective bearing is spread across a wide frequency band and, hence, can easily become masked by other sources of excitation and noise. One of the challenges is to enhance the weak signature at the early stage of defect development.

The proposed method separates bearing signals from signals of shafts, gears, rotors, pumps, etc. (in fact all the discrete frequencies) since the latter usually dominate the spectrum. The technique is based on few stages of resampling and removal of the synchronous time average of the vibration signal. The results show the effectiveness of the method for diagnosis and for automatic bearings’ features extraction both in the orders representation and in the orders of the envelope representation. Subscribe for full docomentation

Environment for processing of wideband signals

Acquisition and analysis of signals from mechanical equipment are necessary steps to achieve the goal of fully automatic diagnostics and prognostics. When some of the data is wide-band, such as signals from vibration and acoustic sensors, the processing stage is computationally intensive and requires a sophisticated handling environment. The article presents a proposed architecture for such an environment. This architecture was developed and used successfully in R.K.

Diagnostics, and is being offered to its customers. The environment is able to support multiple mechanical systems, different configurations of acquisition equipment, automatic data screening, automatic recognition of operation modes, as well as facilitating flexible flows of algorithms configurable for different combinations of flight regimes, plants, or sensors. Other features of the architecture include providing a simple interface for development of diagnostics and prognostics algorithms, ability to store most of the parameters in external database, and ability to export algorithms to customer embedded platform. The system is OSA/CBM compliant, highly modular, platform independent and flexible. Analysis and illustration of the proposed environment is presented for an application of vibrations data analysis using MATLAB. Subscribe for full docomentation

Model Based Approach for Identification of Gears and Bearings Failure Modes

This paper describes the algorithms that were used for analysis of the PHM’09 gear-box. The purpose of the analysis was to detect and identify faults in various components of the gear-box. Each of the 560 vibration recordings presented a different set of faults, including distributed and localized gear faults, typical bearing faults and shaft faults. Each fault had to be pinpointed precisely.

In the following sections we describe the algorithms used for finding faults in bearings, gears and shafts, and the conclusions that were reached. A special blend of pattern recognition and signal processing methods was applied.

Bearings were analyzed using the orders representation of the envelope of a band pass filtered signal and an envelope of the de-phased signal. A special search algorithm was applied for bearings features extraction. The diagnostics of the bearings failure modes was carried out automatically. Gears were analyzed using the order domains, the quefrency of orders, and the derivatives of the phase average. Subscribe for full docomentation