Power plant on-line measured operational data are often corrupted with random and gross errors. The data reconciliation method can reduce the impact of random errors by adjusting redundant measurements to satisfy system constraints and detect gross errors together with a statistical test method. In previous studies, the data reconciliation method is mainly used to deal with measurements with random and gross errors, and its application is mainly in the data preprocessing areas. In this work, we extend the data reconciliation and gross error detection method to cover both sensor and equipment performance monitoring in power plants, through introducing equipment characteristic constraints together with characteristic parameter dominant factor models in the data reconciliation method. The validity and capability of the proposed framework are illustrated with case studies in the feed water regenerative heating system of a 1000 MW ultra-supercritical coal-fired power generation unit. Case study results show that the characteristic parameter dominant factor models have relative root mean squared errors smaller than 2.3%, whilst the distribution properties of the test statistics for the integrated sensor and equipment performance monitoring are validated with simulated test statistic samples. We also illustrate that the proposed framework can efficiently detect and identify both sensor biases and equipment faults in the system. At the same time, the ability of the data reconciliation and global test method for measurement gross error detection is also improved due to the increased system redundancy under the proposed framework. (C) 2014 Elsevier Ltd. All rights reserved.