Advanced signal processing and fault diagnosis in condition monitoring

Lahdelma, Sulo; Juuso, Esko

doi:10.1784/insi.2007.49.12.719

Cited by 19 publications

(53 citation statements)

References 4 publications

(9 reference statements)

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“…Reliable condition monitoring can be achieved when advanced signal processing and automatic fault detection are combined (Lahdelma & Juuso 2007). Intelligent stress and condition indices can be developed by using nonlinear scaling.…”

Section: Vibration Analysis and Signal Processingmentioning

confidence: 99%

Condition-based monitoring for underground mobile machines

Laukka

Saari

Ruuska

et al. 2016

IJISE

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Maintenance operations have significant influence on the economy and performance of mining companies. Unpredicted repairs cause interruptions and breakdowns in production. This means economic losses but, in some cases, also increasing environmental emissions in off-gases and wastewater. Condition based maintenance (CBM) can significantly reduce maintenance costs. Sensors and measurement devices offer a lot of data and assist workers to identify upcoming maintenance needs in advance. Typical measurement variables are for example vibration, temperature, different speeds and pressures. DEVICO project aims to develop a framework for solutions and combine condition monitoring and process data to integrate CBM to control and timing of the maintenance actions. On-line and periodic CM measurements can be combined with process measurements by using signal processing and feature extraction. Case study is conducted in Pyhäsalmi mine with Sandvik load haul dump (LHD) machinery. The condition monitoring system is installed on LHD front axle. The choice for the installation position was made based on the feedback and maintenance data gathered from mining companies. This information indicates that the axles are among the most critical parts in LHD machines.

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Section: Vibration Analysis and Signal Processingmentioning

confidence: 99%

Condition-based monitoring for underground mobile machines

Laukka

Saari

Ruuska

et al. 2016

IJISE

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“…It is very useful when periodic patterns are searched [5]. Vibration analysis also provides information about a particular reason of the fault origin and/or its severity [43]. There is extensive literature demonstrating the development of the method for rolling elements.…”

Section: Fast Fourier Transform (Fft)mentioning

confidence: 99%

Maintenance Management Based on Signal Processing

Márquez¹,

Carrato²,

Pérez³

et al. 2013

Digital Filters and Signal Processing

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Most of the wind turbines are three-blade units (Figure 1.) [55]. Once the wind drives the blades, the energy is transmitted via the main shaft through the gearbox (supported by the bearings) to the generator. The generator speed must be as near as possible to the optimal for the generation of electricity. At the top of the tower, assembled on a base or foundation, the housing or nacelle is mounted and the alignment with the direction of the wind is controlled by a yaw system. There is also a pitch system in each blade. This mechanism controls the wind power and sometimes is employed as an aerodynamic brake. The wind turbine features a hydraulic brake to stop itself when it is needed. Finally, there is a meteorological unit that provides information about the wind (speed and direction) to the control system. 1.1. Maintenance in Wind Turbines Maintenance is a key tool to ensure the operation of all components of a set. One of the objectives is to use available resources efficiently. The classical theory of maintenance was focused on the corrective and preventive maintenance [9] but alternatives to corrective and preventive maintenance have appeared in recent years. One of them is Condition Based Maintenance, which ensures the continuous monitoring and inspection of the wind turbine detecting emerging faults and organizing maintenance tasks that anticipate the failure [59]. Condition Based Maintenance implies acquisition, processing, analysis and interpretation of data and the selection of proper maintenance actions. This is achieved using condition monitoring systems [27, 28]. Thereby, CBM is presented as a useful technique to improve not only the maintenance but the safety of the equipments. Byon and Ding [14] or McMillan and Ault [50] have demonstrated its successful application in wind turbines, making the CBM

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“…In many cases, displacement and velocity measurements are good ways of detecting unbalance or misalignment, for example, and for evaluating their severity [1,2,3] . Bearing faults can be detected more efficiently with acceleration signals, for instance.…”

Section: Introductionmentioning

confidence: 99%

Advanced fault diagnosis by means of complex order derivatives

Laurila¹,

Lahdelma²

2014

Insight

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There is a wide range of signal processing methods in the field of machine condition monitoring, which are used in feature extraction and fault diagnosis. Derivation and integration are very common and important operations in vibration signal processing. Often, a signal that has been measured using an accelerometer is integrated to obtain a velocity or displacement signal. These commonly-used three signals and an infinite number of other signals can be obtained by means of fractional order derivation. Especially in challenging fault and process cases, signals whose order of derivation is a real or complex number could be clearly more sensitive to fault detection than the commonly-used signals. This paper discusses the application of complex order derivation to acceleration signals in order to detect various faults at an early stage.

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Advanced signal processing and fault diagnosis in condition monitoring

Cited by 19 publications

References 4 publications

Condition-based monitoring for underground mobile machines

Condition-based monitoring for underground mobile machines

Maintenance Management Based on Signal Processing

Advanced fault diagnosis by means of complex order derivatives

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