Machine health prognosis based on multiregime condition monitoring signals

This paper presents a proposal for machine health prognosis based on multi regime condition monitoring (CM) signals. The basis idea is performing deeply analysis of CM signals that possibly includes steady state signals – that is normal condition, and developing transient signal that represents some faults exist in the machine. Trigonometric features is extracted from such signals and some energy vectors was used to calculate the health index of machine. Prognosis is then performed on the machine which has the lowest health index, that means the worst condition of the machine. RUL prediction is addressed to estimate the remaining life of the machine up to breakdown. The proposed method gives relatively promising results of RUL prediction that possibility give some times for maintenance actions before catastrophic failure occurs.