Although the new administration in Washington has reversed the “war on coal,” long-term trends in the U.S. are not promising. Most coal-fired capacity was built between 1950 and 1990, and the average coal plant is about 42 years old. With plant retirements expected to continue in 2018 and beyond, investment in new plants has come to a standstill.
The confluence of regulatory issues and alternative energy sources is well known. Because revenue growth is a function of fluctuations in market-based commodity pricing, one of the few remaining opportunities for meaningful bottom-line growth is improving operational efficiencies. Applying Industry 4.0 and machine learning for predictive maintenance could help keep the coal industry financially viable.
There are two types of machine learning methodologies: supervised and unsupervised. With supervised machine learning, the algorithm is “trained” using human guidance and labels of abnormal and normal machine conditions. When new data is analyzed by the algorithm, it can then classify the data as failure indicative, if it recognizes the pattern from its training.
With unsupervised machine learning, the algorithm does not need to be trained using the physical blueprints or knowledge about the process itself. Furthermore, with cloud-based unsupervised machine learning, plant reliability and maintenance staff are alerted to asset degradation and failure without the need for internal Big Data engineers or data scientists to interpret the data. This is an important consideration for coal plants that lack the resources to develop internal competencies in Big Data and machine learning.