Due to the complicated structure and varying operating conditions of machinery in various applications, intelligent identification of the health state based on the vibration data is still a great challenge in fault diagnosis.
This paper presents a new technique for monitoring the condition of rotating machinery from vibration analyses.
It is a predictive maintenance technique that monitors the condition of plant machinery, structures, and systems.
A sensor network with sensors in multiple directions and locations is usually employed for monitoring the condition of rotating machinery.
From one of the company's far-flung logging operations in Russia, for example, a forestry manager can open up his satellite land station, call headquarters and show the boss what the topography and the trees look like or display the condition of the machinery.
Notice things like the age and condition of the machinery, the safety and exposure provided for the machines in their existing locations, and the driving distance between locations.
Next, a weighted similarity measure (WSM) defined in this paper serves to detect a change of running conditions of rotating machinery.
Such signal can be characterized by mono- or multi-component frequency modulation (FM) and its internal instantaneous patterns are closely related to operation condition of the rotary machinery.
Elsewhere, diagnostic research shows that "safety climate onboard the ship, crew self-regulation, crew safety knowledge, safety drill onboard the ship and the condition of ship's machinery" ([32], pp. 74 italics in original) were key factors needing improvement.
As a result, the frequency-smear problem can be eliminated in order spectrum and the fault diagnosis of REB in the varying-speed running condition of the rotating machinery is achieved.
