Adam Jablonski

Bearings Fault Detection in Gas Compressor in Presence of High Level of Non-Gaussian Impulsive Noise

The paper deals with the local damage detection in rolling element bearings in presence of a high level non-Gaussian noise. In many theoretical signal processing papers and engineering application related to damage detection, a simple model of the vibration is assumed. Basically it consists of signal of interest (SOI) and some unwanted (deterministic and/or random) components masking SOI in acquired observation. So, damage detection problem has to concern filtering, decomposition or extraction issue. Unfortunately, in the most of the industrial systems mentioned unwanted sources are in fact not Gaussian, so many of de-noising techniques cannot be applied directly or at least might give unexpected results. In this paper an industrial example will be discussed and novel approach based on advanced cyclostationary-based technique will be proposed. In the paper disturbances include periodic impacts in reciprocating compressor on an oil rig. Existing classical detection techniques (statistics in time domain, analysis of envelope spectrum, time-frequency decompositions) are insufficient to perform the task due to high power of disturbance contribution in comparison to damage signature. In the proposed technique, the Spectral Coherence Density Map (SCDM) is estimated first. Next step is related to analysis of SCDM contents and selection of informative part. If informative and non-informative components lay in separate frequency regions, such selection should fix the problem immediately

Instantaneous Circular Pitch Cyclic Power (ICPCP) – A Tool for Diagnosis of Planetary Gearboxes

The process of monitoring and diagnosis of planetary gearboxes is currently one of the most significant topics for vibroanalysis. The reason for ubiquitous research activities on the topic arises form a shortage of efficient method for a non-destructive thorough vibration analysis of planetary gearboxes, especially working under low speeds. On the other hand, the number of high-value planetary gearboxes working in industry rises rapidly, mainly due to expansion of wind farms. In the paper, the authors present a novel method for the assessment of the technical state of planetary gearboxes with a vibration sensor mounted on the casing, and without a planets position sensor. The method is based on the calculation of cyclic energy of gearbox teeth contact within consecutive carrier revolutions. The methods performance is illustrated on a real data from a wind turbine.

Robust Fragmentation of Vibration Signals for Comparative Analysis in Signal Validation

The paper illustrates selected aspects of robust vibration signal fragmentation. Such fragmentation enables a comparison of vibration signal fragments, which gives additional information about a signal to its frequency contents. In particular, the paper illustrates how signal fragmentation followed by fragments’ comparison may serve as an additional tool in data preprocessing, namely continuous selection of most suitable vibration data and rejection data with prohibited level of vibration level fluctuation. Presented techniques are especially applicable as a part of condition monitoring of machinery in non-stationary operational conditions.

Non-Clustering Method for Automatic Selection of Machine Operational States

A reliable evaluation of technical condition of machinery working under non-stationary conditions requires a rigorous tracking of operational parameters. Therefore, modern condition monitoring systems (CMS) enable reading and registering of process parameters (e.g. speed, load, pressure, etc.) in parallel with acquisition of vibroacoustic signals. Although few tries have been undertaken to develop state-free analysis of vibration signals, currently installed systems still do rely on state-preclassified data. The paper shows how the process, referential data might be automatically transformed into proposition of optimal machine operational states in terms of their number and their range. As indicated by the title, the paper shows common pitfalls coming from implementation of popular clustering approach. The proposed algorithm illustrates is verified on real data from a pitch-controlled wind turbine.

How to Build a Vibration Monitoring System on Your Own

With the ultimate goal of cost reduction of condition monitoring, this paper illustrates how simple data acquisition and processing systems could be designed and realized taking advantage of latest cheap, yet powerful electronic elements. The discussed designs are based on recently popular STM32 and Raspberry Pi boards, and analog MEMS accelerometers. The final prototype design shown in the paper is developed on the F401re version of the STM family, which is working on ARM M4 Cortex processor, and the ADXL001-70 MEMS accelerometer from Analog Devices Ltd. The entire design has been develop using a standard notebook with Windows 10 operating system. The major interest of presenting this design is that in wide range of conditions, the self-made system developed from scratch with elements, price of which does not exceed 15 USD, is capable of generating a frequency spectrum equally significant to a spectrum generated by a full-scale, costly commercial condition monitoring system.

Selection of Suitable Method for Speed Recovery from Vibration Signal

Typically, if order analysis of vibration signal is expected, a speed sensor (phase marker) or an encoder are installed on the shaft. However, in some practical scenarios, the speed information recorded in parallel to the vibration signal acquisition is not available; yet, it is still required. In this case, one is forced to use a raw vibration signal to extract the information about so-called instantaneous phase or instantaneous frequency of a selected component, and—if required—scale to a selected shaft. In recent years, few different techniques for speed recovery have been proposed, each one with different assumptions and each implementing more or less complexed mathematical apparatus. The current paper proposes a guidance how to select a suitable method on the basis of the visual deduction about signal characteristics with the implication on selection of the easiest and most automatized method sufficient for analysed case.

Supervised Classification Methods in Condition Monitoring of Rolling Element Bearings

Operational vibrational diagnostics is crucial for providing the reliability of mid and large scale combustion engine applications (e.g. railway, automotive heavy vehicles or electric generators). This work reports study presenting application of supervised learning and classification methods based on pattern recognition using different classifiers (e.g. logistic regression, k-nearest neighbor or normal density) in order to detect early warning diagnostic symptoms of malfunctioned rolling element bearings (REBs) in the presence of background disturbances from combustion diesel engine. The REB’s malfunction type classification is based on time domain (RMS, peak to peak, Crest factor) as well as frequency domain signal processing methods like envelope analysis or modulation intensity distribution (MID) which allows to neglect the influence of background noise representing by non-stationary operating conditions and possible structural modifications (e.g. maintenance activities or parts replacing). The proposed data classification methods are compared and validated by using experimental measurements conducted on a dedicated combustion engine test bench for wide range of rotational speed and different levels of REB’s radial load.

Knocking Sounds in the Wind Turbine Gearbox During Slowing Down—Case Study

In the gearbox of a wind turbine under investigation, a knocking sound was noticed during coasting down of the machine. The noise was present in one of several gearboxes of the same type and the search for the source of the sound was undertaken. Gearbox manufacturer specialists after an inspection were pointing out sources outside the gearbox —runner unbalance or generator, but the machine owner ordered an additional research comprising vibration measurement and further analyses. The analysis of the vibration signal was carried out with the use of advanced signal analysis tools and the knocking vibration frequency was found to be the same as the frequency of the intermediate shaft. A machine inspection which was carried out pointed at a few potential sources of the sound, but did not specifically determined its source.

Optimization of Calculations for Wireless Condition Monitoring Systems

Health Indicator for machine health monitoring are generally well-established. Regardless of the type of the Condition Monitoring System (stationary, remote, wireless) and the system’s manufacturer, the most commonly applied Health Indicators include wideband estimators (peak-to-peak, Root Mean Square, kurtosis, crest factor, velocity Root Mean Square), narrowband estimators (speed harmonics, gear meshing frequencies, rolling-element bearing characteristic frequencies), and simple spectral bands corresponding to a group of machine elements, e.g. 100–2000 Hz for gearboxes. In order to improve the reliability of Health Indicators, stationary Condition Monitoring System implement averaging and advanced data acquisition logic. In order to detect faults in very early stage, Condition Monitoring System implement resampling, order analysis, Deterministic Random Separation, and for instance auxiliary visualization. However, in case of wireless Condition Monitoring System without a speed sensor, improvement might concern only three aspect, namely hardware realization, data transmission, and power savings, where the latter one might be decomposed into data transfer power consumption, data acquisition power consumption, and data analysis power consumption. The current paper illustrates few recent ideas on how to minimize the power consumptions for data analysis. As it will be shown, it is possible to reduce the computational cycles by more than 60% comparing to stationary Condition Monitoring System while losing acceptable level of the quality of calculated Health Indicators.

On the Research of Extra Characteristic Frequencies in a Planetary Gearbox

Gearboxes have been investigated and monitored for decades since they present one of the important transmission power systems which have been used in navy, air and automotive sectors. One of the most adopted one is the planetary gearbox since it has an important reduction ratio within compact space. The dynamic behaviour of a such one is very complicated because it possesses several gears in mesh and differs from other types of gearboxes by the fact that planet gears can occupy different positions in one period carrier rotation which leads to an important influence on the overall vibration signal acquired by a transducer mounted one the external housing. Consequently, in a measured vibration spectrum, the pass planet frequency component is identified and its energy level is considered only as the pass planet energy. However, there is another phenomenon that increases the level of the pass planet frequency component which is the Open image in new window due to the rotation of planets. In this work, a comprehensive monitoring of a staged planetary gearbox is presented. The unbalance phenomenon is investigated in every stage. Then, an experimental validation is provided in order to support our hypothesis claiming that the Open image in new window phenomenon depends on the parity of the number of planets.