A phenomenon pattern extraction unit extracts a phenomenon pattern of a past sensor signal of a facility. A related information correlation unit correlates the sensor signal based on maintenance history information. A phenomenon pattern classification reference creation unit creates a classification reference for classifying a phenomenon pattern based on the extracted phenomenon pattern and a work keyword included in the maintenance history information correlated with the sensor signal as the source of the phenomenon pattern. A phenomenon pattern classification unit classifies the phenomenon pattern based on the classification reference. A diagnosis model creation unit creates a diagnosis model for estimating a work keyword suggested to a maintenance worker based on the classified phenomenon pattern and the work keyword.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information processing apparatus comprising: a phenomenon pattern extraction unit that extracts a phenomenon pattern of a past sensor signal of a facility; a correlation unit that correlates the sensor signal based on maintenance history information; a classification reference creation unit that creates a classification reference for classifying the phenomenon pattern based on a work keyword included in the maintenance history information correlated with the sensor signal as a source of the phenomenon pattern and the phenomenon pattern extracted by the phenomenon pattern extraction unit; a phenomenon pattern classification unit that classifies the phenomenon pattern based on the classification reference created by the classification reference creation unit; and a diagnosis model creation unit that creates a diagnosis model for estimating a work keyword suggested to a maintenance worker based on the work keyword and the phenomenon pattern classified by the phenomenon pattern classification unit. 2. The information processing apparatus according to claim 1, wherein the phenomenon pattern extraction unit extracts a diagnosis target phenomenon pattern of a diagnosis target sensor signal as the diagnosis target of the facility, the phenomenon pattern classification unit classifies the diagnosis target phenomenon pattern extracted by the phenomenon pattern extraction unit based on the classification reference, and the information processing apparatus further comprises a maintenance work suggestion unit that extracts a work keyword suggested to a maintenance worker by referring to the diagnosis model based on the diagnosis target phenomenon pattern classified by the phenomenon pattern classification unit. 3. The information processing apparatus according to claim 1, wherein the work keyword is extracted based on a correlated value included in the maintenance history information. 4. The information processing apparatus according to claim 3, wherein the maintenance history information includes a maintenance cost or a downtime of the facility, and the correlated value is the maintenance cost or the downtime of the facility. 5. The information processing apparatus according to claim 3, wherein the correlated value is an abnormality measure calculated based on the sensor signal. 6. The information processing apparatus according to claim 3, wherein the correlated value is a numerical value that represents the existence of a specific keyword in the maintenance history information. 7. The information processing apparatus according to claim 1, wherein the classification reference is determined based on a learning method with a teacher by using the work keyword or the combination thereof as a teacher label. 8. The information processing apparatus according to claim 1, wherein the phenomenon pattern is expressed by an accumulation value of a residual vector calculated as a difference between an observation vector and a reference vector from the sensor signal. 9. The information processing apparatus according to claim 1, wherein the phenomenon pattern is expressed by an accumulation value of an isolation vector calculated based on the two-dimensional distribution density of learned data from the sensor signal. 10. The information processing apparatus according to claim 1, wherein the phenomenon pattern is expressed by a histogram characteristic calculated by a bag-of-features method from the sensor signal. 11. The information processing apparatus according to claim 1, wherein the diagnosis model is expressed by the possibility calculated based on the phenomenon pattern classification result and the two-dimensional frequency distribution of the work keyword or the combination thereof. 12. The information processing apparatus according to claim 8, wherein the reference vector is calculated by using a local sub-space classifier. 13. The information processing apparatus according to claim 2, further comprising: an alarm classification reference creation unit that creates an alarm classification reference for classifying the phenomenon pattern by using an alarm included in a past event signal of the facility as a teacher; an alarm classification unit that classifies the phenomenon pattern based on the alarm classification reference created by the alarm classification reference creation unit; an alarm diagnosis model creation unit that creates an alarm diagnosis model for estimating the alarm of the diagnosis target phenomenon pattern based on the alarm and the phenomenon pattern classified by the alarm classification unit; and an alarm estimation unit that estimates the alarm of the diagnosis target phenomenon pattern based on the alarm diagnosis model created by the alarm diagnosis model creation unit, wherein the classification reference creation unit creates the classification reference for each alarm, the diagnosis model creation unit creates the diagnosis model for each alarm, the phenomenon pattern classification unit classifies the diagnosis target phenomenon pattern, in which the alarm is estimated by the alarm estimation unit, based on the classification reference for each alarm, and the maintenance work suggestion unit extracts a work keyword suggested to a maintenance worker by referring to the diagnosis model for each alarm based on the diagnosis target phenomenon pattern classified by the phenomenon pattern classification unit. 14. A method of diagnosing a facility by an information processing apparatus, comprising: extracting a phenomenon pattern of a past sensor signal of the facility; correlating the sensor signal based on maintenance history information; creating a classification reference for classifying the phenomenon pattern based on a work keyword included in the maintenance history information correlated with the sensor signal as a source of the phenomenon pattern and the phenomenon pattern extracted by the extracting of the phenomenon pattern; classifying the phenomenon pattern based on the classification reference created by the creating of the classification reference; and creating a diagnosis model for estimating a work keyword suggested to a maintenance worker based on the work keyword and the phenomenon pattern classified by the classifying of the phenomenon pattern. 15. A non-transitory computer-readable storage medium storing a program that causes an information processing apparatus to function as: a phenomenon pattern extraction unit that extracts a phenomenon pattern of a past sensor signal of a facility; a correlation unit that correlates the sensor signal based on maintenance history information; a classification reference creation unit that creates a classification reference for classifying the phenomenon pattern based on a work keyword included in the maintenance history information correlated with the sensor signal as a source of the phenomenon pattern and the phenomenon pattern extracted by the phenomenon pattern extraction unit; a phenomenon pattern classification unit that classifies the phenomenon pattern based on the classification reference created by the classification reference creation unit; and a diagnosis model creation unit that creates a diagnosis model for estimating a work keyword suggested to a maintenance worker based on the work keyword and the phenomenon pattern classified by the phenomenon pattern classification unit.