Yarn winding equipment (100, 200, 400) used in a phase for production of a package by the yarn winding equipment (100, 200, 400), comprising: a yarn supplying section that can supply a yarn; a winding section that winds the yarn supplied by the yarn supplying section onto a bobbin; a yarn joining device that performs a yarn joining operation to join the yarn that has become discontinuous between the yarn supplying section and the winding section in a yarn running direction; a related information acquiring section that acquires related information that is information related to a tensile strength of a yarn joint of the yarn that has been joined by the yarn joining device, the related information being at least one between information that can be acquired during the yarn joining operation and information that can be acquired while the yarn is running; a model storing section that stores therein a learning model that is constructed beforehand by machine learning by using at least the related information as input data and that is used to estimate a value of the tensile strength of the yarn joint; and a tensile strength estimating section that estimates a tensile strength estimated value of the yarn joint based on the related information by using the learning model, wherein the related information includes at least one type of information among information about dimensions of the yarn joint of the yarn that is running, information about a temperature of a space where the yarn joining device is installed, information about a humidity of the space, information about sounds generated during the yarn joining operation by the yarn joining device, information about a type of the yarn, information about a count of the yarn, and information about settings of the yarn joining device, and wherein the learning model is a model constructed beforehand by executing supervised learning by using teacher data as the machine learning, and the teacher data used in the supervised learning is information about the tensile strength of the yarn joint of the yarn measured beforehand. a yarn supplying section that can supply a yarn; a winding section that winds the yarn supplied by the yarn supplying section onto a bobbin; a yarn joining device that performs a yarn joining operation to join the yarn that has become discontinuous between the yarn supplying section and the winding section in a yarn running direction; a related information acquiring section that acquires related information that is information related to a tensile strength of a yarn joint of the yarn that has been joined by the yarn joining device, the related information being at least one between information that can be acquired during the yarn joining operation and information that can be acquired while the yarn is running; a model storing section that stores therein a learning model that is constructed beforehand by machine learning by using at least the related information as input data and that is used to estimate a value of the tensile strength of the yarn joint; and a tensile strength estimating section that estimates a tensile strength estimated value of the yarn joint based on the related information by using the learning model, wherein the related information includes at least one type of information among information about dimensions of the yarn joint of the yarn that is running, information about a temperature of a space where the yarn joining device is installed, information about a humidity of the space, information about sounds generated during the yarn joining operation by the yarn joining device, information about a type of the yarn, information about a count of the yarn, and information about settings of the yarn joining device, and wherein the learning model is a model constructed beforehand by executing supervised learning by using teacher data as the machine learning, and the teacher data used in the supervised learning is information about the tensile strength of the yarn joint of the yarn measured beforehand.