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.

The paper is concerned with an important issue from the field of thermoacoustics - the numerical modelling of the flow field in the thermoacoustic engine. The presented way of modelling is based on the solution to fundamental fluid mechanics equations that govern the flow of compressible, viscous, and heat-transferring gas. The paper presents the way of modelling the thermoacoustic engine, the way of conducting calculations and the results which illustrate the correctness of the selected computational technique.

The drainage consolidation method has been efficiently used to deal with soft ground improvement. Nowadays, it has been suggested to use a new sand soil which is a composite of sand and recycled glass waste. The permeability performance of glass-sand soil was explored to judge the feasibility of glass-sand soil backfilled in the drainage consolidation of sand-drained ground. For comparison purposes, different mix proportions of recycled glass waste, fineness modulus, and glass particle size were analyzed to certify the impact on the permeability coefficient and the degree of consolidation. The numerical results show that adding a proper amount of recycled glass waste could promote the permeability performance of glass-sand soil, and the glasssand soil drain could be consolidated more quickly than a sand drain. Experiments showed that glass-sand soil with the a 20% mix of recycled glass waste reveals the optimum performance of permeability.