Turbines and generators operating in the power generation industry are a major source of electrical energy worldwide. These are critical machines and their malfunctions should be detected in advance in order to avoid catastrophic failures and unplanned shutdowns. A maintenance strategy which enables to detect malfunctions at early stages of their existence plays a crucial role in facilities using such types of machinery. The best source of data applied for assessment of the technical condition are the transient data measured during start-ups and coast-downs. Most of the proposed methods using signal decomposition are applied to small machines with a rolling element bearing in steady-state operation with a shaft considered as a rigid body. The machines examined in the authors’ research operate above their first critical rotational speed interval and thus their shafts are considered to be flexible and are equipped with a hydrodynamic sliding bearing. Such an arrangement introduces significant complexity to the analysis of the machine behavior, and consequently, analyzing such data requires a highly skilled human expert. The main novelty proposed in the paper is the decomposition of transient vibration data into components responsible for particular failure modes. The method is automated and can be used for identification of turbogenerator malfunctions. Each parameter of a particular decomposed function has its physical representation and can help the maintenance staff to operate the machine properly. The parameters can also be used by the managing personnel to plan overhauls more precisely. The method has been validated on real-life data originating from a 200 MW class turbine. The real-life field data, along with the data generated by means of the commercial software utilized in GE’s engineering department for this particular class of machines, was used as the reference data set for an unbalanced response during the transients in question.

Minimum Entropy Deconvolution (MED) has been recently introduced to the machine condition monitoring field to enhance fault detection in rolling element bearings and gears. MED proved to be an excellent aid to the extraction of these impulses and diagnosing their origin, i.e. the defective component of the bearing. In this paper, MED is revisited and re-introduced with further insights into its application to fault detection and diagnosis in rolling element bearings. The MED parameter selection as well as its combination with pre-whitening is discussed. Two main cases are presented to illustrate the benefits of the MED technique. The first one was taken from a fan bladed test rig. The second case was taken from a wind turbine with an inner race fault. The usage of the MED technique has shown a strong enhancement for both fault detection and diagnosis. The paper contributes to the knowledge of fault detection of rolling element bearings through providing an insight into the usage of MED in rolling element bearings diagnostic. This provides a guide for the user to select optimum parameters for the MED filter and illustrates these on new interesting cases both from a lab environment and an actual case.

Wind turbines are nowadays one of the most promising energy sources. Every year, the amount of energy produced from the wind grows steadily. Investors demand turbine manufacturers to produce bigger, more efficient and robust units. These requirements resulted in fast development of condition-monitoring methods. However, significant sizes and varying operational conditions can make diagnostics of the wind turbines very challenging.

The paper shows the case study of a wind turbine that had suffered a serious rolling element bearing (REB) fault. The authors compare several methods for early detection of symptoms of the failure. The paper compares standard methods based on spectral analysis and a number of novel methods based on narrowband envelope analysis, kurtosis and cyclostationarity approach.

The very important problem of proper configuration of the methods is addressed as well. It is well known that every method requires setting of several parameters. In the industrial practice, configuration should be as standard and simple as possible. The paper discusses configuration parameters of investigated methods and their sensitivity to configuration uncertainties

Focus of the vibration expert community shifts more and more towards diagnosing machines subjected to varying rotational speeds and loads. Such machines require order analysis for proper fault detection and identification. In many cases phase markers (tachometers, encoders, etc) are used to help performing the resampling of the vibration signals to remove the speed fluctuations and smearing from the spectrum (order tracking). However, not all machines have the facility to install speed tracking sensors, due to design or cost reasons, and the signal itself has to then be used to extract this information. This paper is focused on the problem of speed tracking in wind turbines, which represent typical situations for speed and load variation. The basic design of a wind turbine is presented. Two main types of speed control i.e. stall and pitch control are presented,. The authors have investigated two methods of speed tracking, using information from the signal (without relying on a speed signal). One method is based on extracting a reference signal to use as a tachometer, while the other is phase-based (phase demodulation). Both methods are presented and applied to the vibration data from real wind turbines. The results are compared with each other and with the actual speed data.

Condition monitoring of machines working under non-stationary operations is one of the most challenging problems in maintenance. A wind turbine is an example of such class of machines. One of effective approaches may be to identify operating conditions and investigate their influence on used diagnostic features. Commonly used methods based on measurement of electric current, rotational speed, power and other process variables require additional equipment (sensors, acquisition cards) and software. It is proposed to use advanced signal processing techniques for instantaneous shaft speed recovery from a vibration signal. It may be used instead of extra channels or in parallel as signal verification.